This PDF file contains the front matter associated with SPIE Proceedings Volume 7870, including the Title Page, Copyright information, Table of Contents, and the Conference Committee listing.

We propose a powerful video denoising algorithm that exploits temporal and spatial redundancy characterizing natural video sequences. The algorithm implements the paradigm of nonlocal grouping and collaborative filtering, where a higher-dimensional transform-domain representation is leveraged to enforce sparsity and thus regularize the data. The proposed algorithm exploits the mutual similarity between 3-D spatiotemporal volumes constructed by tracking blocks along trajectories defined by the motion vectors. Mutually similar volumes are grouped together by stacking them along an additional fourth dimension, thus producing a 4-D structure, termed group, where different types of data correlation exist along the different dimensions: local correlation along the two dimensions of the blocks, temporal correlation along the motion trajectories, and nonlocal spatial correlation (i.e. self-similarity) along the fourth dimension. Collaborative filtering is realized by transforming each group through a decorrelating 4-D separable transform and then by shrinkage and inverse transformation. In this way, collaborative filtering provides estimates for each volume stacked in the group, which are then returned and adaptively aggregated to their original position in the video. Experimental results demonstrate the effectiveness of the proposed procedure which outperforms the state of the art.

In this paper, we solve the edge enhancement problem using an intelligent approach. We use a multilayer neural network based on multi-valued neurons (MLMVN) as an intelligent edge enhancer. The problem of neural edge enhancement using a classical multilayer feedforward neural network (MLF) was already considered by some authors. Since MLMVN significantly outperforms MLF in terms of learning speed, number of parameters employed, and generalization capability, it is very attractive to apply it for solving the edge enhancement problem. The main result which is presented in the paper, is the proven ability of MLMVN to enhance edges corresponding to a certain edge detection operator. Moreover, it is possible to enhance edges on noisy images ignoring a noisy texture. It is shown that to learn any edge detection operator using MLMVN, only a single image is required for learning purposes. The most important conclusion is that a neural network can learn different edge detection operators from a single example and then process those images that did not participate in the learning process detecting edges specifically corresponding to the learned operator with a high accuracy.

Stack Filters define a large class of discrete nonlinear filter first introduced in image and signal processing for noise removal. In recent years we have suggested their application to classification problems, and investigated their relationship to other types of discrete classifiers such as Decision Trees. In this paper we focus on a continuous domain version of Stack Filter Classifiers which we call Ordered Hypothesis Machines (OHM), and investigate their relationship to Nearest Neighbor classifiers. We show that OHM classifiers provide a novel framework in which to train Nearest Neighbor type classifiers by minimizing empirical error based loss functions. We use the framework to investigate a new cost sensitive loss function that allows us to train a Nearest Neighbor type classifier for low false alarm rate applications. We report results on both synthetic data and real-world image data.

The relationship between the intensity functions of contiguous pixels of an image is used on daily global clouds satellite data to extract local edge gradients for cloud types' classification. The images are cloud top temperatures (CTT) derived from the National Oceanic and Atmospheric Administration/Advanced Very-High-Resolution Radiometer (NOAA-AVHRR) satellite observations. The cloud type classification method used is a histogram-based gradient scheme described as the occurrence of low, mid or high edge gradients in a block of pixels. The distribution of these cloud types is analyzed, then, the consistency of the monthly variations of the cloud type amount estimation is evaluated. A clear dependence of the cloud type amount signal on the solar zenith angle is noticeable. This dependence, due to the gradual satellite drift, is removed through a filtering process using the empirical mode decomposition (EMD) method. The EMD component, associated with the drift or the solar zenith angle change, is filtered out. The cloud types' amount series corrected show a substantial improvement in their trends.

Currently, carpet companies assess the quality of their products based on their appearance retention capabilities. For this, carpet samples with different degrees of wear after a traffic exposure simulation process are rated with wear labels by human experts. Experts compare changes in appearance in the worn samples to samples with original appearance. This process is subjective and humans can make mistakes up to 10% in rating. In search of an objective assessment, research using texture analysis has been conducted to automate the process. Particularly, Local Binary Pattern (LBP) technique combined with a Symmetric adaptation of the Kullback- Leibler divergence (SKL) are successful for extracting texture features related to the wear labels either from intensity and range images. We present in this paper a novel extension of the LBP techniques that improves the representation of the distinct wear labels. The technique consists in detecting those patters that monotonically change with the wear labels while grouping the others. Computing the SKL from these patters considerably increases the discrimination between the consecutive groups even for carpet types where other LBP variations fail. We present results for carpet types representing 72% of the existing references for the EN1471:1996 European standard.

In this paper, a framework for detecting lines in a polished or textured substrate is proposed. Modules for image capture, rectification, enhancement, and line detection are included. If the surface being examined is specular (mirror-like), the image capture will be restricted, that is, the camera has to be fixed off-axis in the zenith direction. A module for image rectification and projection is included to overcome this limitation in order to yield an orthographic image. In addition, a module for image enhancement that includes high-boost is employed to improve the edge sharpness and decrease the spatial noise in the image. Finally, a line-integral technique is applied to find the confidence vectors that represent the spatial positions of the lines of interest. The Full-Width at Half-Max (FWHM) approximation is applied to determine the corresponding lines in a target image. Experimental results show that our technique has an effective performance on synthetic and real images. Print quality assessment is the main application of the proposed algorithm; however, it can be used to detect lines/ streak in prints, on substrate or any type of media where lines are visible.

The analysis of the data provided by LASCO-C2 coronagraph onboard the SOHO spatial observatory revealed the fractal characteristics of many outstanding structures of the solar corona, which is the tiny but extended envelope of plasma wrapping the Sun. A multiscale analysis of recent image sequences has brought a clearer view of the evolution and the local structure of these features which results from a two steps projection process of the 2D electronic distribution over the Sun polar caps. To get an insight in the volume density distribution over these caps and their evolution within time, we used the forward modelling approach based on the present knowledge about the plasma distribution, the physical process of diffusion and the projection geometry on the field of view. The analysis provides us with the multifractal characterization of the observed phenomena. In the forward modelling process the goal is to reconstruct the time sequence of 2D electronic distributions slowly evolving over the Sun polar caps. We used different methodologies: the inverse Fourier transform of 2D+1D (surface and time) frequency modelling, the evolving multiscale synthesis with Gaussian wavelets and the concealed Markov approach. Lately a procedure derivate of the Voss generation schema of fBm fractals has been successfully developed. These different methods are compared and their relative advantages and drawbacks discussed.

Nowadays, sophisticated computer graphics editors lead to a significant increase in the photorealism of images. Thus, computer generated (CG) images result to be convincing and hard to be distinguished from real ones at a first glance. Here, we propose an image forensics technique able to automatically detect local forgeries, i.e., objects generated via computer graphics software inserted in natural images, and vice versa. We develop a novel hybrid classifier based on wavelet based features and sophisticated pattern noise statistics. Experimental results show the effectiveness of the proposed approach.

Forensic science is already taking benefits from synchrotron radiation (SR) sources in trace evidence analysis. In this contribution we show a multi-technique approach to study fingerprints from the morphological and chemical point of view using SR based techniques such as Fourier transform infrared microspectroscopy (FTIRMS), X-ray fluorescence (XRF), X-ray absorption structure (XAS), and phase contrast microradiography. Both uncontaminated and gunshot residue contaminated human fingerprints were deposited on lightly doped silicon wafers and on poly-ethylene-terephthalate foils. For the uncontaminated fingerprints an univariate approach of functional groups mapping to model FT-IRMS data was used to get the morphology and the organic compounds map. For the gunshot residue contaminated fingerprints, after a preliminary elemental analysis using XRF, microradiography just below and above the absorption edge of the elements of interest has been used to map the contaminants within the fingerprint. Finally, XAS allowed us to determine the chemical state of the different elements. The next step will be fusing the above information in order to produce an exhaustive and easily understandable evidence.

There are various scientific and technological areas in which it is imperative to rapidly detect and quantify changes in imagery over time. In fields such as earth remote sensing, aerospace systems, and medical imaging, searching for timedependent, regional changes across deformable topographies is complicated by varying camera acquisition geometries, lighting environments, background clutter conditions, and occlusion. Under these constantly-fluctuating conditions, the use of standard, rigid-body registration approaches often fail to provide sufficient fidelity to overlay image scenes together. This is problematic because incorrect assessments of the underlying changes of high-level topography can result in systematic errors in the quantification and classification of interested areas. For example, in the current naked-eye detection strategies of melanoma, a dermatologist often uses static morphological attributes to identify suspicious skin lesions for biopsy. This approach does not incorporate temporal changes which suggest malignant degeneration. By performing the co-registration of time-separated skin imagery, a dermatologist may more effectively detect and identify early morphological changes in pigmented lesions; enabling the physician to detect cancers at an earlier stage resulting in decreased morbidity and mortality. This paper describes an image processing system which will be used to detect changes in the characteristics of skin lesions over time. The proposed system consists of three main functional elements: 1.) coarse alignment of timesequenced imagery, 2.) refined alignment of local skin topographies, and 3.) assessment of local changes in lesion size. During the coarse alignment process, various approaches can be used to obtain a rough alignment, including: 1.) a manual landmark/intensity-based registration method1, and 2.) several flavors of autonomous optical matched filter methods2. These procedures result in the rough alignment of a patient's back topography. Since the skin is a deformable membrane, this process only provides an initial condition for subsequent refinements in aligning the localized topography of the skin. To achieve a refined enhancement, a Particle Swarm Optimizer (PSO) is used to optimally determine the local camera models associated with a generalized geometric transform. Here the optimization process is driven using the minimization of entropy between the multiple time-separated images. Once the camera models are corrected for local skin deformations, the images are compared using both pixel-based and regional-based methods. Limits on the detectability of change are established by the fidelity to which the algorithm corrects for local skin deformation and background alterations. These limits provide essential information in establishing early-warning thresholds for Melanoma detection. Key to this work is the development of a PSO alignment algorithm to perform the refined alignment in local skin topography between the time sequenced imagery (TSI). Test and validation of this alignment process is achieved using a forward model producing known geometric artifacts in the images and afterwards using a PSO algorithm to demonstrate the ability to identify and correct for these artifacts. Specifically, the forward model introduces local translational, rotational, and magnification changes within the image. These geometric modifiers are expected during TSI acquisition because of logistical issues to precisely align the patient to the image recording geometry and is therefore of paramount importance to any viable image registration system. This paper shows that the PSO alignment algorithm is effective in autonomously determining and mitigating these geometric modifiers. The degree of efficacy is measured by several statistically and morphologically based pre-image filtering operations applied to the TSI imagery before applying the PSO alignment algorithm. These trade studies show that global image threshold binarization provides rapid and superior convergence characteristics relative to that of morphologically based methods.

In medical imaging, magnetic resonance imaging sequences are able to provide information of the damaged brain structure and the neuronal connections. The sequences can be analyzed to form 3D models of the geometry and further including functional information of the neurons of the specific brain area to develop functional models. Modeling offers a tool which can be used for the modeling of brain trauma from images of the patients and thus information to tailor the properties of the transplanted cells. In this paper, we present image-based methods for the analysis of human spinal cord injuries. In this effort, we use three dimensional diffusion tensor imaging, which is an effective method for analyzing the response of the water molecules. This way, our idea is to study how the injury affects on the tissues and how this can be made visible in the imaging. In this paper, we present here a study of spinal cord analysis to two subjects, one healthy volunteer and one spinal cord injury patient. We have done segmentations and volumetric analysis for detection of anatomical differences. The functional differences are analyzed by using diffusion tensor imaging. The obtained results show that this kind of analysis is capable of finding differences in spinal cords anatomy and function.

Ordered halftone patterns in the original document interact with the periodic sampling of the scanner, producing objectionable moir´e patterns. These are exacerbated when the copy is reprinted with an ordered halftone pattern. A simple, small low-pass filter can be used to descreen the image and to correct the majority of moir´e artifacts. Unfortunately, low-pass filtering affects detail as well, blurring it even further. Adaptive nonlinear filtering based on image features such as the magnitude and the direction of image gradient can also be used. However, non careful tuning of such filters could either cause damage to small details while descreeing the halftone areas, or result in less descreening while sharpening small details. In this paper, we present a new segmentation-based descreening technique. Scanned images are segmented into text, images and halftone classes using a multiresolution classification of edge features. The segmentation results guide a nonlinear, adaptive filter to favor sharpening or blurring of image pixels belonging to different classes. Our experimental results show the ability of the non-linear, segmentation driven filter of successfully descreening halftone areas while sharpening small size text contents.

The increasing use of digital image-based applications is resulting in huge databases that are often difficult to use and prone to misuse and privacy concerns. These issues are especially crucial in medical applications. The most commonly adopted solution is the encryption of both the image and the patient data in separate files that are then linked. This practice results to be inefficient since, in order to retrieve patient data or analysis details, it is necessary to decrypt both files. In this contribution, an alternative solution for secure medical image annotation is presented. The proposed framework is based on the joint use of a key-dependent wavelet transform, the Integer Fibonacci-Haar transform, of a secure cryptographic scheme, and of a reversible watermarking scheme. The system allows: i) the insertion of the patient data into the encrypted image without requiring the knowledge of the original image, ii) the encryption of annotated images without causing loss in the embedded information, and iii) due to the complete reversibility of the process, it allows recovering the original image after the mark removal. Experimental results show the effectiveness of the proposed scheme.

Seam carving is a powerful retargeting algorithm for mapping images to arbitrary sizes with arbitrary aspect ratios. Meanwhile, the seamlet transform has been introduced as an efficient image representation for seam-carving-based retargeting over heterogeneous multimedia devices with a broad range of display sizes. The original seamlet transform was developed using Haar filters, and hence it enabled traditional single-seam carving by removing a single seam at a time in a recursive manner until the desired image size was reached. In this paper, we develop a more efficient approach for seam carving by enabling multi-seam carving, where at each step of the retargeting algorithm, multiple seams are carved simultaneously. We achieve multi-seam carving by (a) extending the seamlet transform to allow for larger filters, and (b) employing local circular convolution in the vicinity of the selected seams. We show that by extending the seamlet transform we can employ popular filterbanks such as Daubechies' wavelets to achieve efficient multi-seam carving with visual quality that is comparable to single-seam carving using the Haar transform. Furthermore, with multi-seam carving, the number of iterations needed to achieve a given target size can be reduced significantly.

A new universal low computational complexity algorithm for numerical reconstruction of holograms recorded in near diffraction zone is presented. The algorithm implements digital convolution in DCT domain, which makes it virtually insensitive to boundary effects. It can be used for reconstruction of holograms for arbitrary ratios of hologram size to the object-to-hologram distance and wavelength to camera pitch and allows image reconstruction in arbitrary scale.

The recently developed dual-view touch screens, which are announced to be installed in cars in a near future, give rise to completely new challenges in human-machine interaction. The automotive system should be able to identify if the driver or the passenger is currently interacting with the touch screen to provide a correct response to the touch. The optical devices, due to availability, acceptance by the users and multifunctional usage, approved to be the most appropriate sensing technology for driver/passenger discrimination. In this work the prototypic optical user discrimination system is implemented in the car simulator and evaluated in the laboratory environment with entirely controlled illumination. Three tests were done for this research. One of them examined if the near-infrared illumination should be switched on around the clock, the second one if there is a difference in discrimination performance between day, twilight and night conditions, and the third one examined how the intensive directional lighting influences the performance of the implemented user discrimination algorithm. Despite the high error rates, the evaluation results show that very simple computer vision algorithms are able to solve complicated user discrimination task. The average error rate of 10.42% (daytime with near-infrared illumination) is a very promising result for optical systems.

Interpolation of signals (arbitrary dimension, here: 2D images) with missing data points is addressed from a statistical point of view. We present a general framework for which a Wiener-style MMSE estimator can be seamlessly adapted to deal with problems such as image interpolation (inpainting), reconstruction from sparse samples, and image extrapolation. The proposed method gives a precise answer on a) how arbitrary can linear filters can be applied to initially incomplete signals and b) shows the definite way to extend images beyond theirs borders such that no size reduction occurs if a linear filter/operator is to be applied to the image.

This paper describes an interpolation method that takes into account the edge orientation in order to avoid typical interpolation artifacts (jagging, staircase effects...). It is first based on an edge orientation estimation, performed in the wavelet domain. The estimation uses the multi-resolution features of wavelets to give an accurate and non-biased description of the frequency characteristics of the edges, as well as their orientation. The interpolation is then performed, using the edge orientation estimation, to improve a reference interpolation (cubic-spline for instance). This improvement is carried out by filtering the edges with a Gaussian kernel along their direction in order to smooth the contour in the direction parallel to the edge, which avoids disturbing variations across them (jagging and staircase effects). This technique also keeps the sharpness of the transition in the direction perpendicular to the contour to avoid blur. Results are presented on both synthetic and real images, showing the visual impact of the presented method on the quality of interpolated images. Comparisons are made with the usual cubic-spline interpolation, and with other edge-directed interpolation techniques to discuss the choices that have been made in our method.

This paper describes a novel image reconstruction method based on modified exemplar based technique. This modification allows to choose sub-optimally image-adaptive form and size of the block in order to find similar patches, number of which is further increased by rotation of these blocks. We show that the efficiency of image reconstruction depends on the choice of block size for the exemplar based method. Proposed block size selection adaptivity allows to obtain a smaller reconstruction error than that of the traditional method as well as other state-of-the-art image inpainting methods. We demonstrate the performance of a new approach via several examples, showing the effectiveness of the proposed algorithm in removal of small and large objects on the test images.

The paper provides a method of graph representation of gray scale images. For binary images, it is generally recognized that not only connected components must be captured, but also the holes. For gray scale images, there are two kinds of "connected components" - dark regions surrounded by lighter areas and light regions surrounded by darker areas. These regions are the lower and upper level sets of the gray level function, respectively. The proposed method represents the hierarchy of these sets, and the topology of the image, by means of a graph. This graph contains the well-known inclusion trees, but it is not a tree in general. Two standard topological tools are used. The first tool is cell decomposition: the image is represented as a combination of pixels as well as edges and vertices. The second tool is cycles: both the connected components and the holes are captured by circular sequences of edges.

We do colour image processing in an RGB-derived spherical space with colour attributes given by hue, colourfulness (as opposed to grayness and somewhat different from saturation) and lightness; we call it Runge space. This spherical space is as intuitive as more common spaces of the type hue-saturation-luminance (called here η,Ε,Λ spaces), yet it avoids the continuity problems of the transformation (R, G,B) → (η,Ε,Λ) that result from normalizing the saturation by the luminance, or of having a geometrically nonhomogeneous space, when the saturation is left un-normalized. We give Matlab routines for the conversions between colour spaces RGB and Runge, and present applications of colour modification in Runge space.

Image registration is an important preprocessing technique in high dynamic range (HDR) image synthesis. This paper proposed a robust image registration method for aligning a group of low dynamic range images (LDR) that are captured with different exposure times. Illumination change and photometric distortion between two images would result in inaccurate registration. We propose to transform intensity image data into phase congruency to eliminate the effect of the changes in image brightness and use phase cross correlation in the Fourier transform domain to perform image registration. Considering the presence of non-overlapped regions due to photometric distortion, evolutionary programming is applied to search for the accurate translation parameters so that the accuracy of registration is able to be achieved at a hundredth of a pixel level. The proposed algorithm works well for under and over-exposed image registration. It has been applied to align LDR images for synthesizing high quality HDR images..

Images formed by different systems are often noisy which makes filtering a typical operation of image pre-processing. In many research papers, filter performance is analyzed for a limited number of standard test images and noise variances. Here we use a recently created color image database TID2008 that allows assessing filter efficiency for 25 color images corrupted by noise with different values of variance, both i.i.d. and spatially correlated. Besides, this image database serves the purpose of evaluating different quality metrics including those able to characterize visual quality of original and processed images considerably better than conventional MSE and PSNR. The study is carried out for filters based on discrete cosine transform (DCT) able to suppress both i.i.d. and spatially correlated noise depending upon a way of threshold setting. It is shown that improvement of PSNR (IPSNR) due to filtering is very close for R, G, and B components of color images and this improvement depends on image content. IPSNR reaches 9 dB for quite simple images and it is only about 1 dB for highly textural images if initial PSNR=30 dB. Note that IPSNR is larger if the original PSNR is smaller. The visual quality metric PSNR-HVS-M is studied as well. The metric PSNR-HVS-M becomes larger due to filtering but in smaller degree than PSNR does. We demonstrate that it is possible to forecast whether or not visual quality can be improved due to filtering or to detect in advance highly textural images for which filtering can be not efficient enough. The provided output MSEs are also compared to potential limits calculated according to the recently proposed methodology. It is demonstrated that for highly textural images the DCT filtering with 8x8 full overlapping blocks and hard thresholding provides output MSE close to potential limits. The provided and limit MSEs differ from each other by about 10%. For simpler images, the provided and limit MSEs can differ by 1.5...2.5 times. Analysis is also carried out for spatially correlated noise. It is shown that efficiency of filtering in this case is lower.

The paper deals with JPEG adaptive lossy compression of color images formed by digital cameras. Adaptation to noise characteristics and blur estimated for each given image is carried out. The dominant factor degrading image quality is determined in a blind manner. Characteristics of this dominant factor are then estimated. Finally, a scaling factor that determines quantization steps for default JPEG table is adaptively set (selected). Within this general framework, two possible strategies are considered. A first one presumes blind estimation for an image after all operations in digital image processing chain just before compressing a given raster image. A second strategy is based on prediction of noise and blur parameters from analysis of RAW image under quite general assumptions concerning characteristics parameters of transformations an image will be subject to at further processing stages. The advantages of both strategies are discussed. The first strategy provides more accurate estimation and larger benefit in image compression ratio (CR) compared to super-high quality (SHQ) mode. However, it is more complicated and requires more resources. The second strategy is simpler but less beneficial. The proposed approaches are tested for quite many real life color images acquired by digital cameras and shown to provide more than two time increase of average CR compared to SHQ mode without introducing visible distortions with respect to SHQ compressed images.

Automated panorama creation usually requires camera calibration or extensive knowledge of camera locations and relations to each other. Registration problems are often solved by these same camera parameters or the result of complex point matching schemes. This paper presents a novel automated panorama creation algorithm by using an affine transformation search based on maximized mutual information (MMI). MMI techniques are often limited to airborne and satellite imagery or medical images, but we can show that a simple MMI algorithm very well approximates realistic scenes of varying depth distortion. This study was performed on stationary color surveillance video cameras and proves extremely worthwhile in any system with limited or no a priori camera-to-camera parameters. This algorithm is quite robust on a very large range of strict- to nearly-affine related scenes, and provides a great approximation for the overlap regions in scenes related by a projective homography. Practical considerations were surprisingly significant in molding the development of this robust and versatile algorithm.

This paper proposes an ellipse detection algorithm based on the analytical solution to the parameters of ellipse in images. In the first instance, edge detection is processed, from which line segments are extracted. Then the method of finding the center coordinates of the ellipse is described based on the property of ellipse by using three points voting at a sense of randomized Hough Transformation (RHT). Finally, an analytical solution of the other three parameters of the ellipse (semi-major axis length, semi-minor axis length and the angle between the X-axis and the major axis of the ellipse) are given via coordinate transformation. Based on this solution, we propose the separated parameter voting scheme for ellipse center and the other three parameters instead of 5 parameters voting scheme of RHT. The experiments show that the proposed algorithm performs well in various images.

The blurs in images closely resemble an ideal point spread function (PSF) model. This similarity can be exploited in the deconvolution process by learning a model that best fits the estimated PSF. In order to achieve this, a model is selected from the provided training set and then integrated into the reconstruction cost function. In this paper, we propose to eliminate the need for a training set and instead use a reference PSF (RPSF) in its place. This eliminates the need for specifying a training set as well as the dependence on estimated quantities. Furthermore, it is only dependent on the given degraded image assuming that it is uniformly blurred. The method is tested with motion blurs in different directions since it is one of the most commonly encountered problems when using consumer cameras. Using the blur support as a priori knowledge, the results show that the proposed method is capable of accurately determining the motion direction even in the presence of noise. The reconstruction of the image is achieved by using a modified cost function that also accounts for the contour of the estimated PSF. Results show that higher image quality and lower PSF estimation error can be obtained.

This paper is devoted to a novel hyperparameters estimator for bayesian denoising of images using the Bessel K Forms prior which we recently developed. More precisely, this approach is based on the EM algorithm. The simulation results show that this estimator offers good performances and is slightly better compared to the cumulant-based estimator suggested in. A comparative study is carried to show the effectiveness of our bayesian denoiser based on EM algorithm compared to other denoisers developed in both classical and bayesian contexts. Our study has been effected on natural and medical images for gaussian and poisson noise removal.

This paper presents a new technique for color enhancement based on manipulation of the histogram of logarithmic transform coefficients. The proposed technique is simple but more effective than some existing techniques in most case. This method is based on the properties of the histogram of DCT coefficients, also use the fact that the relationship between stimulus and perception is logarithmic and can afford a marriage between enhancement qualities and computational efficiency. A human visual system-based quantitative measurement of image contrast improvement is also used to determine the optimal parameters for the algorithm. A number of experimental results are presented to illustrate the performance of the proposed algorithm.

In this paper, a novel algorithm for inference in Markov Random Fields (MRFs) is presented. Its goal is to find approximate maximum a posteriori estimates in a simple manner by combining neighbourhood influence of iterated conditional modes (ICM) and message passing of loopy belief propagation (LBP). We call the proposed method neighbourhood-consensus message passing because a single joint message is sent from the specified neighbourhood to the central node. The message, as a function of beliefs, represents the agreement of all nodes within the neighbourhood regarding the labels of the central node. This way we are able to overcome the disadvantages of reference algorithms, ICM and LBP. On one hand, more information is propagated in comparison with ICM, while on the other hand, the huge amount of pairwise interactions is avoided in comparison with LBP by working with neighbourhoods. The idea is related to the previously developed iterated conditional expectations algorithm. Here we revisit it and redefine it in a message passing framework in a more general form. The results on three different benchmarks demonstrate that the proposed technique can perform well both for binary and multi-label MRFs without any limitations on the model definition. Furthermore, it manifests improved performance over related techniques either in terms of quality and/or speed.

Multiscale signal analysis has been used since the early 1990s as a powerful tool for image processing, notably in the linear case. However, nonlinear PDEs and associated nonlinear operators have advantages over linear operators, notably preserving important features such as edges in images. In this paper, we focus on nonlinear Hamilton-Jacobi PDEs defined with adaptive speeds or, alternatively, on adaptive morphological fiters also called semi-flat morphological operators. Semi-flat morphology were instroduced by H. Heijmans and studied only in the case where the speed (or equivalently the filtering parameter) is a decreasing function of the luminance. It is proposed to extend the definition suggested by H. Heijmans in the case of non decreasing speeds. We also prove that a central property for defining morphological filters, that is the adjunction property, is preserved while dealing with our extended definitions. Finally experimental applications are presented on actual images, including connection of thin lines by semi-flat dilations and image filtering by semi-flat openings.

The block-matching and 3-D filtering (BM3D) algorithm is currently one of the most powerful and effective image denoising procedures. It exploits a specific nonlocal image modelling through grouping and collaborative filtering. Grouping finds mutually similar 2-D image blocks and stacks them together in 3-D arrays. Collaborative filtering produces individual estimates of all grouped blocks by filtering them jointly, through transform-domain shrinkage of the 3-D arrays (groups). BM3D can be combined with transform-domain alpha-rooting in order to simultaneously sharpen and denoise the image. Specifically, the thresholded 3-D transform-domain coefficients are modified by taking the alpha-root of their magnitude for some alpha > 1, thus amplifying the differences both within and between the grouped blocks. While one can use a constant (global) alpha throughout the entire image, further performance can be achieved by allowing different degrees of sharpening in different parts of the image, based on content-dependent information. We propose to vary the value of alpha used for sharpening a group through weighted estimates of the low-frequency, edge, and high-frequency content of the average block in the group. This is shown to be a viable approach for image sharpening, and in particular it can provide an improvement (both visually and in terms of PSNR) over its global non-adaptive counterpart.

This paper presents a distributed joint source-channel 3D video coding system. Our aim is the design of an efficient coding scheme for stereoscopic video communication over noisy channels that preserves the perceived visual quality while guaranteeing a low computational complexity. The drawback in using stereo sequences is the increased amount of data to be transmitted. Several methods are being used in the literature for encoding stereoscopic video. A significantly different approach respect to traditional video coding has been represented by Distributed Video Coding (DVC), which introduces a flexible architecture with the design of low complex video encoders. In this paper we propose a novel method for joint source-channel coding in a distributed approach. We choose turbo code for our application and study the new setting of distributed joint source channel coding of a video. Turbo code allows to send the minimum amount of data while guaranteeing near channel capacity error correcting performance. In this contribution, the mathematical framework will be fully detailed and tradeoff among redundancy and perceived quality and quality of experience will be analyzed with the aid of numerical experiments.

As the demand for reduction in the thickness of cameras rises, so too does the interest in thinner lens designs. One such radical approach toward developing a thin lens is obtained from nature's superposition principle as used in the eyes of many insects. But generally the images obtained from those lenses are fuzzy, and require reconstruction algorithms to complete the imaging process. A hurdle to developing such algorithms is that the existing literature does not provide realistic test images, aside from using commercial ray-tracing software which is costly. A solution for that problem is presented in this paper. Here a Gabor Super Lens (GSL), which is based on the superposition principle, is simulated using the public-domain ray-tracing software POV-Ray. The image obtained is of a grating surface as viewed through an actual GSL, which can be used to test reconstruction algorithms. The large computational time in rendering such images requires further optimization, and methods to do so are discussed.

In this paper a novel scheme for extracting the global features from an image. Usually the features are extracted from the whole image. In the proposed approach, only the image regions conveying information are considered. The two steps procedure is based on the Fisher's information evaluation computed by linear combination of Zernike expansion coefficients. Then, by using the region growing algorithm, only high information rate regions are considered. The considered features are texture, edges, and color. The performances of the proposed scheme has been evaluated by using the retrieval rate. Experimental results show an increase in the retrieval rate with respect to use the same features computed on whole image.

An improved RANSAC algorithm using within-class scatter matrix for fast image stitching is proposed in this paper. First, features described by SIFT are extracted. Next, the Min-cost K-flow algorithm is used to match SIFT points in different images. Then, the improved RANSAC algorithm with the within-class scatter matrix is used to divide the matching feature points into two classes: inliers and outliers. Finally, the homography is computed in the set of inliers. Experiment results show that the improved algorithm can increase the registration speed by some 20 percent with the same accuracy and robustness comparing to the original RANSAC algorithm.

Back-to-front interference is a common problem in documents, printed on translucent pages with insufficient opacity and is referred to as bleed through. The present state-of-art algorithms address bleed through based on entropy, entropic correlation and discriminator analysis. However, a common drawback of such algorithms is their inefficient processing of documents that are either sparse in terms of content or have a very dark background. Our proposed algorithm, based on Otsu's binarization method and pixel level classification addresses these problems. Experiments indicate that our algorithm performs comparable to state-of-the-art for most of the images and better than state-of-the-art for the low contrast images.

In this Paper, we introduce a new method for evaluation, quality control, and automatic grading of texture images representing different textural classes of Asphalt Concrete (AC). Also, we present a new asphalt concrete texture grading, wavelet transform, fractal, and Support Vector Machine (SVM) based automatic classification and recognition system. Experimental results were simulated using different cross-validation techniques and achieved an average classification accuracy of 91.4.0 % in a set of 150 images belonging to five different texture grades.

This work proposes a different approach for the use of turning function space to change shapes in accordance with shape descriptions and consistent with spectral information. The main steps are: (1) segmentation; (2) contour extraction; (3) turning function space transform; (4) classification; (5) shape analysis; and (6) blob enhancement on image space. In the analysis of shape the boundary is modified based on both image and model and constraints are imposed to portions of the turning function. Shape modeling can be done by defining criteria such as linearity, angles and sizes. Results on synthetic examples are presented.

In this paper, a speckle noise reduction method is presented. The proposed method is based on a combination of nonlinear anisotropic diffusion filter and Ensemble Empirical Mode Decomposition (EEMD) technique. It incorporates the advantages of the two techniques. The experimental results on the images speckled by various levels of noise show that the proposed method is able to significantly improve the performance of nonlinear anisotropic diffusion filter. Furthermore, it outperforms several well-known speckle reduction algorithms in terms of noise removal as well as image features preservation.

JPEG-LS, the well-known international standard for lossless and near-lossless image compression, was originally designed for non-scalable applications. In this paper we propose a scalable modification of JPEG-LS and compare it with the leading image and video coding standards JPEG2000 and H.264/SVC intra for low-complexity constraints of some wireless video applications including graphics.