Abstract：Concerning the poor quality of existing image super-resolution reconstruction caused by single dictionary, a new single image super-resolution algorithm based on classified image patches and image cartoon-texture decomposition was proposed. Firstly, an image was divided into image patches which were classified into smooth patches, edge patches and texture patches, and the texture class was divided into cartoon part and texture part by Morphological Component Analysis (MCA) algorithm. Secondly, ege patches, cartoon part and texture part of texture patches were applied respectively to train the dictionaries of low-resolution and high-resolution. Finally, the sparse coefficients were calculated, then the image patches were reconstructed by using the corresponding high-resolution dictionary and sparse coefficients. In the comparison experiments with Sparse Coding Super-Resolution (SCSR) algorithm and Single Image Super-Resolution (SISR) algorithm, the Peak Signal-to-Noise Ratio (PSNR) of the proposed algorithm was increased by 0.26 dB and 0.14 dB respectively. The experimental results show that the proposed algorithm can obtain more details in texture with better reconstruction effect.
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2019, Vol. 39 
