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. The main advantage of the proposed method is that it combines the two optimization strategies of image inverse problem. In this paper, two denoisers considered for PnP framework are Recursive Filter and Total Variation. The main advantage of this framework is that it can incorporates any denoiser into it. 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 trained dictionary are derived from set of images, which incorporates the high frequency components. The proposed method aims to reduce blocking artifacts by combining the trained dictionary and Plug and Play (PnP) framework. ![]() The JPEG compression is one of the traditional approach to produces compression at higher compression rates, despite the decompression still yields blocking artifacts.
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