Abstract
A modified sparse representation based image super-resolution reconstruction (ISR) is discussed in this paper. The edge features of high resolution (HR) image patches and the gradient and texture features of low resolution (LR) image patches are considered in our method. Meanwhile, features of LR image patches are classified by extreme learning machine (ELM) classifier. Further, For image patches’ features classified, the fast sparse coding (FSC) algorithm based K-SVD sparse representation is used to train sparse dictionaries. And utilized these dictionaries, LR images can be super-resolution reconstructed well. Simulation results show that our method has clear improvement in visual effect and retain well image detail.
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