Super resolution (SR) is to produce a higher resolution image from one or a sequence of low resolution images of a scene. It is essential in medical image analysis as a zooming of a specific area of interest is often required. This paper presents a new multi-frame super resolution (SR) method that is robust to both global and local motion. One of major challenges in multi-frame SR is concurrent global and local motion emergent in the sequence of low resolution images. It poses difficulties in aligning the low resolution images, resulting in artifacts or blurred pixels in the computed high resolution image. We solve the problem via a series of new methods. We first align the upscaled images from bicubic interpolation, and analyze the pixel distribution for the presence of local motion. If local motion is identified, we conduct the local image registration using dense SIFT features. Based on the local registration of images, we analyze pixel locations whose cross-frame variation is high and adaptively select subset of frame pixels in those locations. The adaptive selection of frame pixels is based on a clustering analysis of luminance values of pixels aligned at the same position, such that noise and motion biases are excluded. At the end, a median filter is applied for the selected pixels at each pixel location for super resolution image. We conduct experiments for multi-frame SR, where the proposed method delivers favorable results, especially better than state-of-the-art in dealing with concurrent local and global motions across frames.
In this paper, we develop a server-client quantization scheme to reduce bit resolution of deep learning architecture, i.e., Convolutional Neural Networks, for image recognition tasks. Low bit resolution is an important factor in bringing the deep learning neural network into hardware implementation, which directly determines the cost and power consumption. We aim to reduce the bit resolution of the network without sacrificing its performance. To this end, we design a new quantization algorithm called supervised iterative quantization to reduce the bit resolution of learned network weights. In the training stage, the supervised iterative quantization is conducted via two steps on server – apply k-means based adaptive quantization on learned network weights and retrain the network based on quantized weights. These two steps are alternated until the convergence criterion is met. In this testing stage, the network configuration and low-bit weights are loaded to the client hardware device to recognize coming input in real time, where optimized but expensive quantization becomes infeasible. Considering this, we adopt a uniform quantization for the inputs and internal network responses (called feature maps) to maintain low on-chip expenses. The Convolutional Neural Network with reduced weight and input/response precision is demonstrated in recognizing two types of images: one is hand-written digit images and the other is real-life images in office scenarios. Both results show that the new network is able to achieve the performance of the neural network with full bit resolution, even though in the new network the bit resolution of both weight and input are significantly reduced, e.g., from 64 bits to 4-5 bits.
Dong-Ki Min, Ilia Ovsiannikov, Yohwan Noh, Wanghyun Kim, Sunhwa Jung, Joonho Lee, Deokha Shin, Hyekyung Jung, Lawrence Kim, Grzegorz Waligorski, Lilong Shi, Yoondong Park, Chilhee Chung
3D time-of-flight depth cameras utilize modulated light sources to detect the distance to objects as phase information. A serious limitation may exist in cases when multiple depth time-of-flight cameras are imaging the same scene simultaneously. The interference caused by the multiple modulated light sources can severely distort captured depth images. To prevent this problem and enable concurrent 3D multi-camera imaging, we propose modulating the camera light source and demodulating the received signal using sequences of pulses, where the phase of each sequence is varied in a pseudo-random fashion. The proposed algorithm is mathematically derived and proved by experiment.
In this paper, we proposed a new technique for demosaicing a unique RGBZ color-depth imaging sensor, which
captures color and depth images simultaneously, with a specially designed color-filter-array (CFA) where two out of
six RGB color rows are replaced by “Z” pixels that capture depth information but no color information. Therefore,
in an RGBZ image, the red, green and blue colors are more sparsely sampled than in a standard Bayer image. Due to
the missing rows in the data image, commonly used demosaicing algorithms for the standard Bayer CFA cannot be
applied directly. To this end, our method first fills-in the missing rows to reconstruct a full Bayer CFA, followed by
a color-selective adaptive demosaicing algorithm that interpolates missing color components. In the first step, unlike
common bilinear interpolation approaches that tend to blur edges, our edge-based directional interpolation approach,
derived from de-interlacing techniques, emphasizes reconstructing more straight and sharp edges with fewer
artifacts and thereby preserves the vertical resolution in the reconstructed the image. In the second step, to avoid
using the newly estimated pixels for demosaicing, the bilateral-filter-based approach interpolates the missing color
samples based on weighted average of adaptively selected known pixels from the local neighborhoods. Tests show
that the proposed method reconstructs full color images while preserving edges details, avoiding artifacts, and
removing noise with high efficiency.
KEYWORDS: Reflectivity, RGB color model, Error analysis, Cameras, Digital imaging, Statistical analysis, Image compression, Sensors, Data modeling, Algorithm development
The problem of illumination estimation for color constancy and automatic white balancing of digital color imagery can be viewed as the separation of the image into illumination and reflectance components. We propose using nonnegative matrix factorization with sparseness constraints to separate these components. Since illumination and reflectance are combined multiplicatively, the first step is to move to the logarithm domain so that the components are additive. The image data is then organized as a matrix to be factored into nonnegative components. Sparseness constraints imposed on the resulting factors help distinguish illumination from reflectance. The proposed approach provides a pixel-wise estimate of the illumination chromaticity throughout the entire image. This approach and its variations can also be used to provide an estimate of the overall scene illumination chromaticity.
KEYWORDS: Cameras, Error analysis, RGB color model, Databases, Digital filtering, High dynamic range imaging, Electronic imaging, Image compression, Digital cameras, Distance measurement
The performance of the MaxRGB illumination-estimation method for color constancy and
automatic white balancing has been reported in the literature as being mediocre at best;
however, MaxRGB has usually been tested on images of only 8-bits per channel. The question
arises as to whether the method itself is inadequate, or rather whether it has simply been
tested on data of inadequate dynamic range. To address this question, a database of sets of
exposure-bracketed images was created. The image sets include exposures ranging from very
underexposed to slightly overexposed. The color of the scene illumination was determined by
taking an extra image of the scene containing 4 Gretag Macbeth mini Colorcheckers placed at
an angle to one another. MaxRGB was then run on the images of increasing exposure. The
results clearly show that its performance drops dramatically when the 14-bit exposure range of
the Nikon D700 camera is exceeded, thereby resulting in clipping of high values. For those
images exposed such that no clipping occurs, the median error in MaxRGB's estimate of the
color of the scene illumination is found to be relatively small.
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