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Compressed Image Restoration by Combining Trained Dictionary with Plug and Play Framework

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Abstract

The JPEG compression is one of the traditional approach to produces compression at higher compression rates, despite the decompression still yields blocking artifacts. The proposed method aims to reduce blocking artifacts by combining the trained dictionary and Plug and Play (PnP) framework. The trained dictionary are derived from set of images, which incorporates the high frequency components. The PnP framework is based on image inverse problem, and this framework finds the optimized solution using Alternating Direction based method and leading denoisers. The main advantage of this framework is that it can incorporates any denoiser into it. In this paper, two denoisers considered for PnP framework are Recursive Filter and Total Variation. The main advantage of the proposed method is that it combines the two optimization strategies of image inverse problem. The trained dictionary finds the optimized solution based on the greedy approach and the PnP frame finds the optimized solution based on the constrained optimization. Specifically, the results are compared with leading techniques and sparsified DCT dictionary with PnP framework. The proposed method effectively restore the medical images that were compressed using JPEG format.

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  1. Department of CSE, Noorul Islam Centre for Higher Education, Kumaracoil, Tamilnadu, India

    M. S. Sujithra

  2. Department of IT, Noorul Islam Centre for Higher Education, Kumaracoil, Tamilnadu, India

    N. Sugitha

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Sujithra, M.S., Sugitha, N. Compressed Image Restoration by Combining Trained Dictionary with Plug and Play Framework. Wireless Pers Commun 124, 2809–2829 (2022). https://doi.org/10.1007/s11277-022-09490-8

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