A new approach for restoring block-transform coded images with estimation of correlation matrices

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Graph Spectral Image Processing

Recent advent of graph signal processing (GSP) has spurred intensive studies of signals that live naturally on irregular data kernels described by graphs (e.g., social networks, wireless sensor networks). Though a digital image contains pixels that reside on a regularly sampled 2D grid, if one can design an appropriate underlying graph connecting pixels with weights that reflect the image structure, then one can interpret the image (or image patch) as a signal on a graph, and apply GSP tools for processing and analysis of the signal in graph spectral domain. In this article, we overview recent graph spectral techniques in GSP specifically for image / video processing. The topics covered include image compression, image restoration, image filtering and image segmentation

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

Multi-scale edge-guided image gap restoration

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University London.The focus of this research work is the estimation of gaps (missing blocks) in digital images. To progress the research two main issues were identified as (1) the appropriate domains for image gap restoration and (2) the methodologies for gap interpolation. Multi-scale transforms provide an appropriate framework for gap restoration. The main advantages are transformations into a set of frequency and scales and the ability to progressively reduce the size of the gap to one sample wide at the transform apex. Two types of multi-scale transform were considered for comparative evaluation; 2-dimensional (2D) discrete cosines (DCT) pyramid and 2D discrete wavelets (DWT). For image gap estimation, a family of conventional weighted interpolators and directional edge-guided interpolators are developed and evaluated. Two types of edges were considered; ‘local’ edges or textures and ‘global’ edges such as the boundaries between objects or within/across patterns in the image. For local edge, or texture, modelling a number of methods were explored which aim to reconstruct a set of gradients across the restored gap as those computed from the known neighbourhood. These differential gradients are estimated along the geometrical vertical, horizontal and cross directions for each pixel of the gap. The edge-guided interpolators aim to operate on distinct regions confined within edge lines. For global edge-guided interpolation, two main methods explored are Sobel and Canny detectors. The latter provides improved edge detection. The combination and integration of different multi-scale domains, local edge interpolators, global edge-guided interpolators and iterative estimation of edges provided a variety of configurations that were comparatively explored and evaluated. For evaluation a set of images commonly used in the literature work were employed together with simulated regular and random image gaps at a variety of loss rate. The performance measures used are the peak signal to noise ratio (PSNR) and structure similarity index (SSIM). The results obtained are better than the state of the art reported in the literature

DC coefficient restoration for transform image coding.

by Tse, Fu Wing.Thesis (M.Phil.)--Chinese University of Hong Kong, 1996.Includes bibliographical references (leaves 155-[63]).Acknowledgment --- p.iiiAbstract --- p.ivContents --- p.viList of Tables --- p.xList of Figures --- p.xiiNotations --- p.xviiChapter 1 --- Introduction --- p.1Chapter 1.1 --- DC coefficient restoration --- p.1Chapter 1.2 --- Model based image compression --- p.5Chapter 1.3 --- The minimum edge difference criterion and the existing estima- tion schemes --- p.7Chapter 1.3.1 --- Fundamental definitions --- p.8Chapter 1.3.2 --- The minimum edge difference criterion --- p.9Chapter 1.3.3 --- The existing estimation schemes --- p.10Chapter 1.4 --- Thesis outline --- p.14Chapter 2 --- A mathematical description of the DC coefficient restoration problem --- p.17Chapter 2.1 --- Introduction --- p.17Chapter 2.2 --- Mathematical formulation --- p.18Chapter 2.3 --- Properties of H --- p.22Chapter 2.4 --- Analysis of the DC coefficient restoration problem --- p.22Chapter 2.5 --- The MED criterion as an image model --- p.25Chapter 2.6 --- Summary --- p.27Chapter 3 --- The global estimation scheme --- p.29Chapter 3.1 --- Introduction --- p.29Chapter 3.2 --- the global estimation scheme --- p.30Chapter 3.3 --- Theory of successive over-relaxation --- p.34Chapter 3.3.1 --- Introduction --- p.34Chapter 3.3.2 --- Gauss-Seidel iteration --- p.35Chapter 3.3.3 --- Theory of successive over-relaxation --- p.38Chapter 3.3.4 --- Estimation of optimal relaxation parameter --- p.41Chapter 3.4 --- Using successive over-relaxation in the global estimation scheme --- p.43Chapter 3.5 --- Experiments --- p.48Chapter 3.6 --- Summary --- p.49Chapter 4 --- The block selection scheme --- p.52Chapter 4.1 --- Introduction --- p.52Chapter 4.2 --- Failure of the minimum edge difference criterion --- p.53Chapter 4.3 --- The block selection scheme --- p.55Chapter 4.4 --- Using successive over-relaxation with the block selection scheme --- p.57Chapter 4.5 --- Practical considerations --- p.58Chapter 4.6 --- Experiments --- p.60Chapter 4.7 --- Summary --- p.61Chapter 5 --- The edge selection scheme --- p.65Chapter 5.1 --- Introduction --- p.65Chapter 5.2 --- Edge information and the MED criterion --- p.66Chapter 5.3 --- Mathematical formulation --- p.70Chapter 5.4 --- Practical Considerations --- p.74Chapter 5.5 --- Experiments --- p.76Chapter 5.6 --- Discussion of edge selection scheme --- p.78Chapter 5.7 --- Summary --- p.79Chapter 6 --- Performance Analysis --- p.81Chapter 6.1 --- Introduction --- p.81Chapter 6.2 --- Mathematical derivations --- p.82Chapter 6.3 --- Simulation results --- p.92Chapter 6.4 --- Summary --- p.96Chapter 7 --- The DC coefficient restoration scheme with baseline JPEG --- p.97Chapter 7.1 --- Introduction --- p.97Chapter 7.2 --- General specifications --- p.97Chapter 7.3 --- Simulation results --- p.101Chapter 7.3.1 --- The global estimation scheme with the block selection scheme --- p.101Chapter 7.3.2 --- The global estimation scheme with the edge selection scheme --- p.113Chapter 7.3.3 --- Performance comparison at the same bit rate --- p.121Chapter 7.4 --- Computation overhead using the DC coefficient restoration scheme --- p.134Chapter 7.5 --- Summary --- p.134Chapter 8 --- Conclusions and Discussions --- p.136Chapter A --- Fundamental definitions --- p.144Chapter B --- Irreducibility by associated directed graph --- p.146Chapter B.1 --- Irreducibility and associated directed graph --- p.146Chapter B.2 --- Derivation of irreducibility --- p.147Chapter B.3 --- Multiple blocks selection --- p.149Chapter B.4 --- Irreducibility with edge selection --- p.151Chapter C --- Sample images --- p.153Bibliography --- p.15

Low frequency coefficient restoration for image coding.

by Man-Ching Auyeung.Thesis (M.Phil.)--Chinese University of Hong Kong, 1997.Includes bibliographical references (leaves 86-93).Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Transform coding and the JPEG scheme --- p.2Chapter 1.2 --- Motivation --- p.5Chapter 1.3 --- Thesis outline --- p.6Chapter 2 --- MED and DC Coefficient Restoration scheme --- p.8Chapter 2.1 --- Introduction --- p.8Chapter 2.2 --- MED and DC Coefficient Restoration scheme --- p.10Chapter 2.2.1 --- Definition --- p.10Chapter 2.2.2 --- Existing schemes --- p.11Chapter 2.3 --- DC Coefficient Restoration scheme using block selection scheme --- p.14Chapter 2.4 --- Joint optimization technique --- p.16Chapter 2.4.1 --- Lagrange multiplier method --- p.17Chapter 2.4.2 --- Algorithm description --- p.18Chapter 2.5 --- Experimental results --- p.20Chapter 2.6 --- Summary --- p.32Chapter 3 --- Low Frequency Walsh Transform Coefficient Restoration scheme --- p.34Chapter 3.1 --- Introduction --- p.34Chapter 3.2 --- Restoration of low frequency coefficient using Walsh transform --- p.35Chapter 3.3 --- Selection of quantization table optimized for Walsh transform --- p.37Chapter 3.3.1 --- Image model used --- p.39Chapter 3.3.2 --- Infinite uniform quantization --- p.40Chapter 3.3.3 --- Search for an optimized quantization matrix --- p.42Chapter 3.4 --- Walsh transform-based LFCR scheme --- p.44Chapter 3.5 --- Experimental results --- p.46Chapter 3.6 --- Summary --- p.56Chapter 4 --- Low Frequency DCT Coefficient Prediction --- p.57Chapter 4.1 --- Introduction --- p.57Chapter 4.2 --- Low Frequency Coefficient Prediction scheme with negligible side information --- p.58Chapter 4.2.1 --- Selection of threshold --- p.63Chapter 4.2.2 --- Representation of the AC component --- p.63Chapter 4.3 --- Experimental results --- p.67Chapter 4.4 --- Summary --- p.84Chapter 5 --- Conclusions --- p.86Appendix A --- p.89Bibliography --- p.9

Study and simulation of low rate video coding schemes

The semiannual report is included. Topics covered include communication, information science, data compression, remote sensing, color mapped images, robust coding scheme for packet video, recursively indexed differential pulse code modulation, image compression technique for use on token ring networks, and joint source/channel coder design