Color constancy algorithms can provide us with illuminant invariant descriptions for a scene, and it is often accomplished by illuminant estimation. Most statistics-based methods estimate the illuminant color from the information provided by all pixels of an image. However, this research reveals that, for most images, the color of many pixels is quite different from the illuminant, and these pixels may severely trim the performance of statistics-based methods. Based on this fact, we propose a color constancy algorithm that finds a subset of image pixels with r, g components similar to those of the illuminant through a shallow neural network, and this subset of pixels is called illuminant close pixels (ICPs). Then the illuminant color is estimated from these pixels by some statistics-based methods. The proposed method has been evaluated and investigated on two benchmark datasets. Compared to using all pixels in an image, these statistics-based methods have been efficiently improved using ICPs.
KEYWORDS: Prototyping, Reflectivity, Digital imaging, Digital color imaging, RGB color model, Cameras, Color difference, Principal component analysis, Imaging spectroscopy, Statistical analysis
Digital imaging has become a very important technique in the conservation of cultural art relics because it can nondestructively acquire the color and spectral image of cultural art relics for different applications. Imaging accuracy is one of the key factors in digital protection of cultural art relics. In order to improve the color and spectral accuracy for digital imaging of cultural art relics, the idea of making the specific color charts for different kinds of artworks is presented. Taking ancient Chinese Dunhuang murals as the specific object of study, a prototype pigments color chart of the Dunhuang murals (DCC), containing a six-step grayscale and 30 colored pigment samples, is made to investigate its pigment types and painting techniques. Under the premise of considering the difference in the number of samples in color charts, the DCC is tested and compared with the classic and widely used standard Macbeth colorchecker (CC) in two aspects: color correction for RGB imaging and spectral reconstruction for spectral imaging. The results show that the prototype pigments color chart is more effective and exhibits superior performance to the CC in both aspects for digital conservation of the Dunhuang murals.
The existing methods—such as sieving, microscope, light scattering, sedimentation, and electrical induction for pigment size detection—require sampling or scattering the mineral pigments, which will inevitably cause damage to the films painted by mineral pigments. A new detection method based on run length texture analysis is proposed to nondestructively detect the pigment size in the mineral paint film. The films painted by mineral pigments with preknown pigment sizes are contactlessly captured by CCD microscope under diffused light. Gray transform, histogram equalization, and median filtering are implemented to preprocess the captured images, and then the run length texture parameters are extracted from the preprocessed images. A parametric relationship between the extracted parameters and the preknown size is established to predict the pigment size in mineral paint film nondestructively. Burnt carnelian is selected as the sample to verify the feasibility of the proposed method. Results show that the max detection error of the proposed method is 5.548 μm and can be applied to the size detection of the mineral pigments used in mineral paint film.
KEYWORDS: Digital watermarking, Halftones, Image quality, Image processing, Visual process modeling, Printing, Human vision and color perception, Visualization, Data hiding, Systems modeling
The paper discussed the digital watermarking algorithm which embeds watermarking in halftone image in the process of
halftoning. The digital watermarking algorithm based on human vision system (HVS) model which can minimize the
visual error between the embedded watermarking halftone image and the original continuous-tone image using iterative
binary search method. The algorithm can embed large amount of information in halftone image under the precondition
of watermarking invisibility; on the other hand, the extraction of watermarking is reliable. All experiments indicate that
this algorithm can embed more information than other algorithm and has strong robustness, so the algorithm can resist
unconsciously or intentionally attacks which caused by printing, scan, dirty, clip, etc., at the same time, the
watermarking embedded in halftone image using the algorithms has the advantage of invisibility, high vision quality of
halftone image.
Digital halftoning algorithm is a operation, converting the captured con-tone images to the corresponding binary images
supported by most output devices, which makes the tow kinds of images similar as possible. In order to evaluate the
halftoning algorithms and the corresponding halftones, a criterion must be needed. In the literature, MSE (the Mean
Square Error), SNR (Signal to Noise Ratio) and WSNR(Weight Signal to Noise Ratio) were often used to evaluate the
common con-tone images and the halftones. But these methods do not suit to evaluating the quality of the halftones
because of the special properties of the halftones by different halftoning algorithms and limitation of assumption of these
methods themselves according to many researches. So a series of halftonig algorithm-based methods are proposed,
which adapt to the special properties of halftoning algorithms. All of those methods were not adaptive. In the last part of
this paper, an adaptive method was propose to evaluate the halftoning algorithms and the corresponding halftones, which
is based on the statistical features of the residual image between the original image and the corresponding halftone on the
retinal of human eye.
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