PROJECT TITLE :
Single Image Super-Resolution Using Compressive Sensing With a Redundant Dictionary
In medical imaging and astronomical observation, high-resolution (HR) pictures are urgently desired and required. Lately, many researchers have proposed various ways that to realize the goal of image super-resolution (SR), starting from straightforward linear interpolation schemes to nonlinear complicated methods. In this paper, we have a tendency to deal with the SR reconstruction downside primarily based on the idea of compressive sensing, that uses a redundant dictionary instead of a conventional orthogonal basis. We any demonstrate better results on true pictures in terms of peak signal-to-noise ratio (PSNR) and root-mean-square error (RMSE) and provide many vital enhancements, compared with different strategies.
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