A Novel Approach for Image Deblurring

In the area of image processing blur removal is essential step in image quality enhancement .It also has real time applications, therefore it is necessary to have efficient method to remove blur. We have proposed a non linear blur model which simply models low light pixels. In this work we have applied Gaussian kernel instead of Laplacian kernel. The proposed method is developed in such a way that it automatically detects low light pixel from a given blurred image. It also suppress the ringing artifacts. The more accurate results are obtained on problematic and challenging blur images

On the Richardson-Lucy Algorithm with A Varying Point Spread Function along The Iterations

Abstract: This work analyses the soundness of two algorithms, Fishsint and Almexp to improve images using the Richardson-Lucy (RL) algorithm under a varying Point Spread Function (PSF) along the iterations. A plethora of methods based on Richardson-Lucy has been published, but no further proposal involving such an alternative has been published. Whereas the unnamed predecessor of Fishsint addressed only small size synthetic images under a blind fashion procedure, Fishsint and Almexp employs an algorithm Wdet to determine the initial PSF and all subsequent values after each iteration, respectively. Fishsint performs a loop, where the last determined PSF improves the previously obtained image and vice versa. Its original unnamed version has been modified in the present work by entering a previously determined initial PSF to accelerate the convergence. The algorithm Almexp, as well, uses the algorithm Wdet to determine the PSF of the last obtained image to deconvolve itself. Therefore, whereas the Fishsint unnamed predecessor used an initial guess PSF - chosen by the customer - Almexp determines the PSF always through the algorithm Wdet. Fishsint and Almexp furnish final images which outperform those obtained with the original Richardson-Lucy approach working under a constant PSF along the iterations. Hence, in order to carry out a comparison between their performances, all the algorithms have been embedded into an ad hoc written Fortran 90 program. The results corroborate the soundness of a varying PSF along the iterations with the Richardson Lucy algorithm. Keywords: Richardson-Lucy, varying PSF along iterations, image improvement, neutron radiograph

Recent Progress in Image Deblurring

This paper comprehensively reviews the recent development of image deblurring, including non-blind/blind, spatially invariant/variant deblurring techniques. Indeed, these techniques share the same objective of inferring a latent sharp image from one or several corresponding blurry images, while the blind deblurring techniques are also required to derive an accurate blur kernel. Considering the critical role of image restoration in modern imaging systems to provide high-quality images under complex environments such as motion, undesirable lighting conditions, and imperfect system components, image deblurring has attracted growing attention in recent years. From the viewpoint of how to handle the ill-posedness which is a crucial issue in deblurring tasks, existing methods can be grouped into five categories: Bayesian inference framework, variational methods, sparse representation-based methods, homography-based modeling, and region-based methods. In spite of achieving a certain level of development, image deblurring, especially the blind case, is limited in its success by complex application conditions which make the blur kernel hard to obtain and be spatially variant. We provide a holistic understanding and deep insight into image deblurring in this review. An analysis of the empirical evidence for representative methods, practical issues, as well as a discussion of promising future directions are also presented.Comment: 53 pages, 17 figure

Recent Progress in Image Deblurring

This paper comprehensively reviews the recent development of image deblurring, including non-blind/blind, spatially invariant/variant deblurring techniques. Indeed, these techniques share the same objective of inferring a latent sharp image from one or several corresponding blurry images, while the blind deblurring techniques are also required to derive an accurate blur kernel. Considering the critical role of image restoration in modern imaging systems to provide high-quality images under complex environments such as motion, undesirable lighting conditions, and imperfect system components, image deblurring has attracted growing attention in recent years. From the viewpoint of how to handle the ill-posedness which is a crucial issue in deblurring tasks, existing methods can be grouped into five categories: Bayesian inference framework, variational methods, sparse representation-based methods, homography-based modeling, and region-based methods. In spite of achieving a certain level of development, image deblurring, especially the blind case, is limited in its success by complex application conditions which make the blur kernel hard to obtain and be spatially variant. We provide a holistic understanding and deep insight into image deblurring in this review. An analysis of the empirical evidence for representative methods, practical issues, as well as a discussion of promising future directions are also presented

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