Image Restoration Using Joint Statistical Modeling in Space-Transform Domain

This paper presents a novel strategy for high-fidelity image restoration by characterizing both local smoothness and nonlocal self-similarity of natural images in a unified statistical manner. The main contributions are three-folds. First, from the perspective of image statistics, a joint statistical modeling (JSM) in an adaptive hybrid space-transform domain is established, which offers a powerful mechanism of combining local smoothness and nonlocal self-similarity simultaneously to ensure a more reliable and robust estimation. Second, a new form of minimization functional for solving image inverse problem is formulated using JSM under regularization-based framework. Finally, in order to make JSM tractable and robust, a new Split-Bregman based algorithm is developed to efficiently solve the above severely underdetermined inverse problem associated with theoretical proof of convergence. Extensive experiments on image inpainting, image deblurring and mixed Gaussian plus salt-and-pepper noise removal applications verify the effectiveness of the proposed algorithm.Comment: 14 pages, 18 figures, 7 Tables, to be published in IEEE Transactions on Circuits System and Video Technology (TCSVT). High resolution pdf version and Code can be found at: http://idm.pku.edu.cn/staff/zhangjian/IRJSM

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

An Impressive Method to Get Better Peak Signal Noise Ratio (PSNR), Mean Square Error (MSE) Values Using Stationary Wavelet Transform (SWT)

Impulse noise in images is present because of bit errors in transmission or introduced during the signal acquisition stage. There are two types of impulse noise, they are salt and pepper noise and random valued noise. In our proposed method, first we apply the Stationary wavelet transform for noise added image. It will separate into four bands like LL, LH, HL and HH. The proposed algorithm replaces the noisy pixel by trimmed median value when other pixel values, 02019;s and 2552019;s are present in the selected window and when all the pixel values are 02019;s and 2552019;s then the noise pixel is replaced by mean value of all the elements present in the selected window. This proposed algorithm shows better results than the Standard median filter (MF), decision based algorithm (DBA). The proposed method performs well in removing low to medium density impulse noise with detail preservation up to a noise density of 70% and it gives better Peak signal-to-noise ratio (PSNR) and mean square error (MSE) values

Real-time filtering and detection of dynamics for compression of HDTV

The preprocessing of video sequences for data compressing is discussed. The end goal associated with this is a compression system for HDTV capable of transmitting perceptually lossless sequences at under one bit per pixel. Two subtopics were emphasized to prepare the video signal for more efficient coding: (1) nonlinear filtering to remove noise and shape the signal spectrum to take advantage of insensitivities of human viewers; and (2) segmentation of each frame into temporally dynamic/static regions for conditional frame replenishment. The latter technique operates best under the assumption that the sequence can be modelled as a superposition of active foreground and static background. The considerations were restricted to monochrome data, since it was expected to use the standard luminance/chrominance decomposition, which concentrates most of the bandwidth requirements in the luminance. Similar methods may be applied to the two chrominance signals

Highlights Analysis System (HAnS) for low dynamic range to high dynamic range conversion of cinematic low dynamic range content

We propose a novel and efficient algorithm for detection of specular reflections and light sources (highlights) in cinematic content. The detection of highlights is important for reconstructing them properly in the conversion of the low dynamic range (LDR) to high dynamic range (HDR) content. Highlights are often difficult to be distinguished from bright diffuse surfaces, due to their brightness being reduced in the conventional LDR content production. Moreover, the cinematic LDR content is subject to the artistic use of effects that change the apparent brightness of certain image regions (e.g. limiting depth of field, grading, complex multi-lighting setup, etc.). To ensure the robustness of highlights detection to these effects, the proposed algorithm goes beyond considering only absolute brightness and considers five different features. These features are: the size of the highlight relative to the size of the surrounding image structures, the relative contrast in the surrounding of the highlight, its absolute brightness expressed through the luminance (luma feature), through the saturation in the color space (maxRGB feature) and through the saturation in white (minRGB feature). We evaluate the algorithm on two different image data-sets. The first one is a publicly available LDR image data-set without cinematic content, which allows comparison to the broader State of the art. Additionally, for the evaluation on cinematic content, we create an image data-set consisted of manually annotated cinematic frames and real-world images. For the purpose of demonstrating the proposed highlights detection algorithm in a complete LDR-to-HDR conversion pipeline, we additionally propose a simple inverse-tone-mapping algorithm. The experimental analysis shows that the proposed approach outperforms conventional highlights detection algorithms on both image data-sets, achieves high quality reconstruction of the HDR content and is suited for use in LDR-to-HDR conversion

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

Conditional toggle mappings: principles and applications

International audienceWe study a class of mathematical morphology filters to operate conditionally according to a set of pixels marked by a binary mask. The main contribution of this paper is to provide a general framework for several applications including edge enhancement and image denoising, when it is affected by salt-and-pepper noise. We achieve this goal by revisiting shock filters based on erosions and dilations and extending their definition to take into account the prior definition of a mask of pixels that should not be altered. New definitions for conditional erosions and dilations leading to the concept of conditional toggle mapping. We also investigate algebraic properties as well as the convergence of the associate shock filter. Experiments show how the selection of appropriate methods to generate the masks lead to either edge enhancement or salt-and-pepper denoising. A quantitative evaluation of the results demonstrates the effectiveness of the proposed methods. Additionally, we analyse the application of conditional toggle mapping in remote sensing as pre-filtering for hierarchical segmentation

Underwater image restoration: super-resolution and deblurring via sparse representation and denoising by means of marine snow removal

Underwater imaging has been widely used as a tool in many fields, however, a major issue is the quality of the resulting images/videos. Due to the light's interaction with water and its constituents, the acquired underwater images/videos often suffer from a significant amount of scatter (blur, haze) and noise. In the light of these issues, this thesis considers problems of low-resolution, blurred and noisy underwater images and proposes several approaches to improve the quality of such images/video frames. Quantitative and qualitative experiments validate the success of proposed algorithms