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Single image super-resolution based on sparse representation using dictionaries trained with input image patches

In this study, an efficient self-learning method for image super-resolution (SR) is presented. In the proposed algorithm, the input image is divided into equal size patches. Using these patches, a dictionary is learned based on K-SVD, referred to as high resolution (HR) dictionary. Then, by down-sampling, the columns of the dictionary, called atoms, a low resolution (LR) version of the dictionary is obtained. An initial estimate of the SR image is constructed using the bicubic interpolation on the input image. Then in an iterative algorithm, the difference between the down-sampled version of the estimated SR image and the input image is obtained. This difference image, which includes reconstructed details is enlarged using sparse representation and LR/HR dictionaries. The enlarged detail is added to the latest reconstructed SR image. This process gradually improves the quality of the initial SR image. After several iterations, the reconstructed image is an SR version of the input image. Experimental results confirm that the proposed method performance is promising.