Super-resolution image reconstruction method based on deep convolutional sparse coding

Abstract

An SR image reconstruction method based on deep convolutional sparse coding (DCSC) is provided. The method includes: embedding a multi-layer learned iterative soft thresholding algorithm (ML-LISTA) of a multi-layer convolutional sparse coding (ML-CSC) model into a deep convolutional neural network (DCNN), adaptively updating all parameters of the ML-LISTA with a learning ability of the DCNN, and constructing an SR multi-layer convolutional sparse coding (SRMCSC) network which is an interpretable end-to-end supervised neural network for SR image reconstruction; and introducing residual learning, extracting a residual feature with the ML-LISTA, and reconstructing a high-resolution (HR) image in combination with the residual feature and an input image, thereby accelerating a training speed and a convergence speed of the SRMCSC network. The SRMCSC network provided by the present disclosure has the compact structure and the desirable interpretability, and can generate visually attractive results to offer a practical solution for the SR reconstruction.

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