2014/01/31 by Jian Zhang, Debin Zhao, Ruiqin Xiong +2 · 189 citations
Computer Science · Engineering · Mathematics · #Advanced Image Fusion Techniques #Advanced Image Processing Techniques #Artificial intelligence #Computer science #Computer vision #Domain (mathematical analysis) #Engineering #Image (mathematics) #Image and Signal Denoising Methods #Image processing #Image restoration #Joint (building) #Mathematical analysis #Mathematics #Pattern recognition (psychology) #cs.CV #cs.MM
paper · pdf · doi:10.1109/tcsvt.2014.2302380
published in IEEE Transactions on Circuits and Systems for Video Technology 24(6), 915-928 (Institute of Electrical and Electronics Engineers) · 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/
openalex publication_date 2014/01/31 · arxiv created 2014/05/11 · arxiv updated 2014/05/14 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/05
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-fold. 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 the image inverse problem is formulated using JSM under a 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.