Image Restoration Using Joint Statistical Modeling in Space-Transform Domain

Abstract

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.

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/

References in corpus (3)

• Fast Image Recovery Using Variable Splitting and Constrained Optimization
• Image Deblurring and Super-resolution by Adaptive Sparse Domain Selection and Adaptive Regularization
• Image Compressive Sensing Recovery Using Adaptively Learned Sparsifying Basis via L0 Minimization

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• Analyzing the Weighted Nuclear Norm Minimization and Nuclear Norm Minimization based on Group Sparse Representation
• Group Sparsity Residual Constraint for Image Denoising
• Non-Convex Weighted Lp Nuclear Norm based ADMM Framework for Image Restoration
• A Benchmark for Sparse Coding: When Group Sparsity Meets Rank Minimization
• A Comparative Study for the Nuclear Norms Minimization Methods
• MARLow: A Joint Multiplanar Autoregressive and Low-Rank Approach for Image Completion
• From Group Sparse Coding to Rank Minimization: A Novel Denoising Model for Low-level Image Restoration
• WTDUN: Wavelet Tree-Structured Sampling and Deep Unfolding Network for Image Compressed Sensing