Single Image Super-Resolution Reconstruction Network With Dual Attention Mechanism

Abstract

Aiming at the problem that the solution space of mapping function from low resolution image to high resolution image is extremely large, which makes it difficult for super-resolution reconstruction models to generate detailed textures, this paper proposes a single image super resolution reconstruction network combining dual attention mechanism. The improved U-Net network model is used as the basic structure, and the data enhancement method is introduced to increase the diversity of samples. The encoder is composed of a convolution layer and an adaptive parameter linear rectifier function (Dynamic ReLU). The image size is decreased step by step through the subsampling operation. The decoder part was composed of Residual Dual Attention Module (RDAM) and Pixelshuffle Module. The image was enlarged gradually through the up-sampling operation, and dual regression loss was introduced to enhance the network constraints. The experimental results show that the proposed method can make the reconstructed image texture more detailed and reduce the possible solution space of the mapping function effectively.