HIGH-QUALITY IMAGE RESTORATION USING LOW-RANK PATCH REGULARIZATION AND GLOBAL STRUCTURE SPARSITY

Today there has been big improvement in digital technology that has led to the development of various usable devices and methods especially in the fields of communications and data transfer to a longer distance. The transmission of data in the form of documents, images and so on is now reachable to all parts of the society and the services are affordable to most of the people. An important aspect is data Compression & restoration and for that matter Image Compression & restoration, as images form a larger part of data being exchanged over the internet through social networking and information sharing apps all over the world. Among different kinds of data, images and videos constitute the bulkiest data. Thus, need for compressing the image files is an important aspect in data communication. The image Compression & restoration is the technique which is applied to compress the pixels of the image to reduce size without compromising image quality. The work contains improvement in vector based Fuzzy DCT algorithm Compression & restoration technique to increase Compression & restoration ratio. In this research work we present a technique for image Compression & restoration, using Fuzzy Discrete Cosine Transform and Fuzzy Logic Techniques. The algorithm used in this project is tested along with several images and the results are compared with other techniques. Our method shows an improved performance both in Compression & restoration ratio as well as image perceptibility. The performance of proposed will be implemented in java. The results shows better PSNR and Compression & restoration ratio as compare to discrete cosine transformation . high-frequency residual component, is presented to recover missing pixels while preserving structural information in the image. A effective optimization technique, supported the Alternating Direction technique of Multipliers (ADMM) rule, is employed to resolve the planned model. Experimental results show our technique to shell progressive image completion approaches, for varied text-corrupted pictures and completely different ratios of missing pixels. We presented a novel image completion method that exploits the similarity of nonlocal patches in the image, and applies an innovative global structure regularization technique to guide the reconstruction process. The nonlocal self-similarity (NSS) prior uses weighted nuclear norm to regularize groups of similar patches, while retaining the texture and edge information of these patches. Moreover, the world structure regularization technique projected during this paper preserves the structural info of the image by imposing sparsity on the image’s high-frequency residual part. An efficient optimization strategy, based on the ADMM algorithm, was proposed to recover the image from a corrupted observation. Experiments on several benchmark images have shown our method to outperform state-of-the-art image completion approaches, for various ratios of missing pixels and text corruptions. In future work, the proposed method could be extended to other image reconstruction problems, such as image super-resolution.