Hybrid Approach of Nu-Mob, Mobil and MOBILAP for Face Recognition System

This is the first attempt to systematically address face recognition under (i) non-uniform motion blur and (ii) the combined effects of blur, illumination and pose. In this paper, we propose a methodology for face recognition in the presence of space-varying motion blur comprising of kernels. We model the blurred face as a convex combination of geometrically transformed instances of the focused gallery face, and show that the set of all images obtained by non-uniformly blurring a given image forms a convex set. We first propose a non-uniform blur-robust algorithm by blurring the gallery image’s with its corresponding TSF function and extract LBP features and finally returns the identity of the probe image by comparing the LBP features of the probe image with those of the transformed gallery images and find the closest match. Then we propose the motion blur and illumination-robust algorithm by blurring and re-illuminating the gallery image’s with its corresponding optimal TSF function and illumination coefficients and extract LBP features and finally returns the identity of the probe image. Finally we propose the motion, blur, illumination and pose-robust algorithm by estimating and synthesizing the new pose of the blurred probe image and then blurring and re-illuminating the gallery image’s with its corresponding optimal TSF function and illumination coefficients and extract LBP features and finally finds the closest match of the given input probe image

Learning Moore-Penrose based residuals for robust non-blind image deconvolution

This work was supported by grants P20_00286 and B-TIC-324-UGR20 funded by Consejería de Universidad, Investigación e Innovación ( Junta de Andalucía ) and by “ ERDF A way of making Europe”. Funding for open access charge: Universidad de Granada / CBUA.This paper proposes a deep learning-based method for image restoration given an inaccurate knowledge of the degradation. We first show how the impulse response of a Wiener filter can approximate the Moore-Penrose pseudo-inverse of the blur convolution operator. The deconvolution problem is then cast as the learning of a residual in the null space of the blur kernel, which, when added to the Wiener restoration, will satisfy the image formation model. This approach is expected to make the network capable of dealing with different blurs since only residuals associated with the Wiener filter have to be learned. Artifacts caused by inaccuracies in the blur estimation and other image formation model inconsistencies are removed by a Dynamic Filter Network. The extensive experiments carried out on several synthetic and real image datasets assert the proposed method's performance and robustness and demonstrate the advantage of the proposed method over existing ones.Junta de Andalucía P20_00286, B-TIC-324-UGR20ERDF A way of making EuropeUniversidad de Granada / CBU

A Novel Algorithm For Similarity Calculation Of Image Patches

Coordinating composite representations with advanced face pictures is a testing issue which is of great enthusiasm to law authorization organizations. An algorithm was shown that uses multi-scale highlight extraction utilizing visual saliency, texture components, and credit feedback to match composite representations with advanced pictures. The algorithm uses notable patches to concentrate surface components from both computerized picture and composite portrayal. These texture elements are consolidated together to perform texture based coordinating. The qualities of the portrayal and the picture are used to figure a characteristic match score that is melded with the texture match score. The outcomes demonstrate that distinctive parts of the proposed algorithm contribute towards enhancing the identification exactness

Progressive Joint Low-light Enhancement and Noise Removal for Raw Images

Low-light imaging on mobile devices is typically challenging due to insufficient incident light coming through the relatively small aperture, resulting in a low signal-to-noise ratio. Most of the previous works on low-light image processing focus either only on a single task such as illumination adjustment, color enhancement, or noise removal; or on a joint illumination adjustment and denoising task that heavily relies on short-long exposure image pairs collected from specific camera models, and thus these approaches are less practical and generalizable in real-world settings where camera-specific joint enhancement and restoration is required. To tackle this problem, in this paper, we propose a low-light image processing framework that performs joint illumination adjustment, color enhancement, and denoising. Considering the difficulty in model-specific data collection and the ultra-high definition of the captured images, we design two branches: a coefficient estimation branch as well as a joint enhancement and denoising branch. The coefficient estimation branch works in a low-resolution space and predicts the coefficients for enhancement via bilateral learning, whereas the joint enhancement and denoising branch works in a full-resolution space and progressively performs joint enhancement and denoising. In contrast to existing methods, our framework does not need to recollect massive data when being adapted to another camera model, which significantly reduces the efforts required to fine-tune our approach for practical usage. Through extensive experiments, we demonstrate its great potential in real-world low-light imaging applications when compared with current state-of-the-art methods

Motion blur removal from photographs

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 135-143).One of the long-standing challenges in photography is motion blur. Blur artifacts are generated from relative motion between a camera and a scene during exposure. While blur can be reduced by using a shorter exposure, this comes at an unavoidable trade-off with increased noise. Therefore, it is desirable to remove blur computationally. To remove blur, we need to (i) estimate how the image is blurred (i.e. the blur kernel or the point-spread function) and (ii) restore a natural looking image through deconvolution. Blur kernel estimation is challenging because the algorithm needs to distinguish the correct imageblur pair from incorrect ones that can also adequately explain the blurred image. Deconvolution is also difficult because the algorithm needs to restore high frequency image contents attenuated by blur. In this dissertation, we address a few aspects of these challenges. We introduce an insight that a blur kernel can be estimated by analyzing edges in a blurred photograph. Edge profiles in a blurred image encode projections of the blur kernel, from which we can recover the blur using the inverse Radon transform. This method is computationally attractive and is well suited to images with many edges. Blurred edge profiles can also serve as additional cues for existing kernel estimation algorithms. We introduce a method to integrate this information into a maximum-a-posteriori kernel estimation framework, and show its benefits. Deconvolution algorithms restore information attenuated by blur using an image prior that exploits a heavy-tailed gradient profile of natural images. We show, however, that such a sparse prior does not accurately model textures, thereby degrading texture renditions in restored images. To address this issue, we introduce a content-aware image prior that adapts its characteristics to local textures. The adapted image prior improves the quality of textures in restored 6 images. Sometimes even the content-aware image prior may be insufficient for restoring rich textures. This issue can be addressed by matching the restored image's gradient distribution to its original image's gradient distribution, which is estimated directly from the blurred image. This new image deconvolution technique called iterative distribution reweighting (IDR) improves the visual realism of reconstructed images. Subject motion can also cause blur. Removing subject motion blur is especially challenging because the blur is often spatially variant. In this dissertation, we address a restricted class of subject motion blur: the subject moves at a constant velocity locally. We design a new computational camera that improves the local motion estimation and, at the same time, reduces the image information loss due to blur.by Taeg Sang Cho.Ph.D

Deep learning-based diagnostic system for malignant liver detection

Cancer is the second most common cause of death of human beings, whereas liver cancer is the fifth most common cause of mortality. The prevention of deadly diseases in living beings requires timely, independent, accurate, and robust detection of ailment by a computer-aided diagnostic (CAD) system. Executing such intelligent CAD requires some preliminary steps, including preprocessing, attribute analysis, and identification. In recent studies, conventional techniques have been used to develop computer-aided diagnosis algorithms. However, such traditional methods could immensely affect the structural properties of processed images with inconsistent performance due to variable shape and size of region-of-interest. Moreover, the unavailability of sufficient datasets makes the performance of the proposed methods doubtful for commercial use. To address these limitations, I propose novel methodologies in this dissertation. First, I modified a generative adversarial network to perform deblurring and contrast adjustment on computed tomography (CT) scans. Second, I designed a deep neural network with a novel loss function for fully automatic precise segmentation of liver and lesions from CT scans. Third, I developed a multi-modal deep neural network to integrate pathological data with imaging data to perform computer-aided diagnosis for malignant liver detection. The dissertation starts with background information that discusses the proposed study objectives and the workflow. Afterward, Chapter 2 reviews a general schematic for developing a computer-aided algorithm, including image acquisition techniques, preprocessing steps, feature extraction approaches, and machine learning-based prediction methods. The first study proposed in Chapter 3 discusses blurred images and their possible effects on classification. A novel multi-scale GAN network with residual image learning is proposed to deblur images. The second method in Chapter 4 addresses the issue of low-contrast CT scan images. A multi-level GAN is utilized to enhance images with well-contrast regions. Thus, the enhanced images improve the cancer diagnosis performance. Chapter 5 proposes a deep neural network for the segmentation of liver and lesions from abdominal CT scan images. A modified Unet with a novel loss function can precisely segment minute lesions. Similarly, Chapter 6 introduces a multi-modal approach for liver cancer variants diagnosis. The pathological data are integrated with CT scan images to diagnose liver cancer variants. In summary, this dissertation presents novel algorithms for preprocessing and disease detection. Furthermore, the comparative analysis validates the effectiveness of proposed methods in computer-aided diagnosis