Channel (digital image)

About: Channel (digital image) is a research topic. Over the lifetime, 7211 publications have been published within this topic receiving 69974 citations.

A reference-free underwater image quality assessment metric in frequency domain

Abstract: Owing to the complexity of the underwater environment and the limitations of imaging devices, the quality of underwater images varies differently, which may affect the practical applications in modern military, scientific research, and other fields. Thus, achieving subjective quality assessment to distinguish different qualities of underwater images has an important guiding role for subsequent tasks. In this paper, considering the underwater image degradation effect and human visual perception scheme, an effective reference-free underwater image quality assessment metric is designed by combining the colorfulness, contrast, and sharpness cues. Specifically, inspired by the different sensibility of humans to high-frequency and low-frequency information, we design a more comprehensive color measurement in spatial domain and frequency domain. In addition, for the low contrast caused by the backward scattering, we propose a dark channel prior weighted contrast measure to enhance the discrimination ability of the original contrast measurement. The sharpness measurement is used to evaluate the blur effect caused by the forward scattering of the underwater image. Finally, these three measurements are combined by the weighted summation, where the weighed coefficients are obtained by multiple linear regression. Moreover, we collect a large dataset for underwater image quality assessment for testing and evaluating different methods. Experiments on this dataset demonstrate the superior performance both qualitatively and quantitatively.

Integrated circuit failure analysis using color voltage contrast

Abstract: A method and apparatus for analyzing failures in integrated circuits. A first image is obtained using an emission or electron microscope while an integrated circuit is operating under a first set of conditions. The image is integrated for improved resolution with a camera in front of the microscope screen or with a digitizer coupled to receive video signals from the microscope. The first image is digitized and stored in a first channel of an RGB digitizer board and displayed on a display screen. A second image is obtained in the same way and is digitized and stored in a second channel of the RGB digitizer board and displayed on the display screen. The remaining channel of the RGB digitizer board is coupled to receive live images. The resulting combined image appears as a black and white image so long as the images are aligned. Any differences between the three images will appear conspicuously in color. The input logic levels to the integrated circuit are changed. Nodes having changed logic levels will appear in color in the display because they will only affect the third channel. In addition, the displayed image will simultaneously show nodes which have not changed states in different shades of grey depending upon the unchanged logic level. The displayed image may then be compared to a previously obtained reference image from an integrated circuit known to not have any defects. Any differences between the two images will indicate the exact location of a failure or defect.

The application of nonlocal total variation in image denoising for mobile transmission

Abstract: Image transmission is one of the key techniques in image mobile communication. However, it is generally corrupted by noise in wireless channel, which will decrease the visual quality and affect the sub-sequential applications, such as pattern recognition, classification and so on. Total variation is widely used in the problems of image denoising, due to its advantage in preserving texture in image. In this paper, a novel minimization framework is presented where the objective function includes an usual l2 data-fidelity term and two types of total variation regularizer. According to the theory analysis, the novel objective function can preserve the local geometric structure in restored image. Furthermore, we proposes to solve the novel framework with majorization- minimization and compares this novel algorithm with some current restoration method. The numerical experiments show the efficiency and effectiveness of the proposed algorithm.

Deep High-Resolution Representation Learning for Cross-Resolution Person Re-Identification

Abstract: Person re-identification (re-ID) tackles the problem of matching person images with the same identity from different cameras. In practical applications, due to the differences in camera performance and distance between cameras and persons of interest, captured person images usually have various resolutions. This problem, named Cross-Resolution Person Re-identification, presents a great challenge for the accurate person matching. In this paper, we propose a Deep High-Resolution Pseudo-Siamese Framework (PS-HRNet) to solve the above problem. Specifically, we first improve the VDSR by introducing existing channel attention (CA) mechanism and harvest a new module, i.e., VDSR-CA, to restore the resolution of low-resolution images and make full use of the different channel information of feature maps. Then we reform the HRNet by designing a novel representation head, HRNet-ReID, to extract discriminating features. In addition, a pseudo-siamese framework is developed to reduce the difference of feature distributions between low-resolution images and high-resolution images. The experimental results on five cross-resolution person datasets verify the effectiveness of our proposed approach. Compared with the state-of-the-art methods, the proposed PS-HRNet improves the Rank-1 accuracy by 3.4%, 6.2%, 2.5%,1.1% and 4.2% on MLR-Market-1501, MLR-CUHK03, MLR-VIPeR, MLR-DukeMTMC-reID, and CAVIAR datasets, respectively, which demonstrates the superiority of our method in handling the Cross-Resolution Person Re-ID task. Our code is available at https://github.com/zhguoqing .

Methods for identifying, separating and editing reflection components in multi-channel images and videos

Abstract: The present invention presents a framework for separating specular and diffuse reflection components in images and videos. Each pixel of the an M-channel input image illuminated by N light sources is linearly transformed into a new color space having (M-N) channels. For an RGB image with one light source, the new color space has two color channels (U,V) that are free of specularities and a third channel (S) that contains both specular and diffuse components. When used with multiple light sources, the transformation may be used to produce a specular invariant image. A diffuse RGB image can be obtained by applying a non-linear partial differential equation to an RGB image to iteratively erode the specular component at each pixel. An optional third dimension of time may be added for processing video images. After the specular and diffuse components are separated, dichromatic editing may be used to independently process the diffuse and the specular components to add or suppress visual effects. The (U,V) channels of images can be used as input to 3-D shape estimation algorithms including shape-from-shading, photometric stereo, binocular and multinocular stereopsis, and structure-from-motion.