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@article{143621,
        author = {V.Sujini Goud},
        title = {An amended Joint Super-Resolution and Deblocking for a  Compressed Images},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {2},
        number = {12},
        pages = {453-456},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=143621},
        abstract = {Super-resolution imaging (SR) is a class of techniques that improve the resolution of an imaging system. A highly compressed image is typically not only of lowresolution, but also undergoes from compression artifacts. In this paper, we suggest a learning-based framework to accomplish joint single-image SR and deblocking for a highly-compressed image. We say that individually performing deblocking and SR (i.e., deblocking followed by SR, or SR followed by deblocking) on a highly compressed image usually cannot achieve a satisfactory visual quality. In our method, we suggest to learn image sparse representations for modeling the relationship between low and high-resolution image patches in terms of the learned dictionaries for image patches with and without blocking artifacts, respectively.},
        keywords = {Image super-resolution, sparse representation,dictionary learning, self-learning, image decomposition},
        month = {},
        }