The invention discloses a multi-description compressed
image enhancement method based on residual
recursion compensation and
feature fusion, belongs to the field of
image quality enhancement, and solves the problem of different degrees of compression
distortion of an image compressed by a multi-description coding method, especially the problem of serious structure splitting artifacts of a side decoded image. The method comprises the following steps: firstly, designing a residual recursive compensation network as a low-resolution
feature extraction network of a side path and a middle path, and more effectively extracting two description decoding image features with the same content and different details by using a parameter sharing strategy; secondly, enabling the multi-description side feature up-sampling reconstruction network to adopt a network part layer parameter sharing strategy, so that the size of a
network model is greatly reduced, and the generalization ability of the network is improved. Meanwhile, a multi-description middle-path feature up-sampling reconstruction network is used for performing deep
feature fusion on two side-path low-resolution features and a middle-path low-resolution feature, so that efficient multi-description compressed
image quality enhancement is realized, and the performance of the method is superior to that of a plurality of
deep learning image enhancement methods such as ARCNN, FastARCNN and DnCNN.