An image processing method, system, terminal, and medium

Abstract

This application provides an image processing method, system, medium, and terminal. The method includes: acquiring a training image set consisting of a degraded image and a clear image; constructing an optimization problem and designing iterative solution steps using an alternating proximal linearization method; designing iterative solution steps using an alternating proximal linearization method, alternately updating sparse representation coefficients, a local dictionary, and a globally reconstructed image; expanding the iterative solution steps into a deep neural network, with each stage corresponding to one iteration, including cascaded sparse coding, dictionary updating, and image reconstruction sub-modules; using the degraded image as input to the deep neural network and the clear image as the target, and optimizing the network parameters using a backpropagation algorithm; inputting the degraded image to be processed into the trained deep neural network, and outputting a restored image by weighted superposition of the product of the local dictionary and sparse coefficients with the globally reconstructed image. This application can guarantee the global convergence of the algorithm and can significantly improve the performance of image denoising, deblurring, and restoration.

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