Abstract
According to the higher computational complexity during the training process of sparse representation, the centers of granular computing (GrC) with L1-norm are regarded as the bases of sparse representation and used to reconstruct the super-resolution image of input image. Firstly, the granule is represented as the shape of hyperdiamond by the L1-norm in N-dimensional space. Secondly, the join operation between two hyperdiamond granules is designed to transform the microcosmic world into the macroscopic world. Thirdly, the threshold of granularity is used to control the join process. The centers of granules are regarded as the approximate bases to reconstruct the super-resolution (SR) image of the low-resolution (LR) image. Experimental results show that the SR image reconstruction by GrC with L1-norm reduced the root mean square error (RMSE) between the SR image and the original image compared with the bicubic interpolation and sparse representation.
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