Super-Resolution Reconstruction Based on Adaptive Weight Adjustment

Abstract

In image super-resolution, the existing convolution neural network methods increase the number of network layers and filters to achieve better performance, and seldom consider the influence of different branches in feature extraction on the reconstruction effect, which leads to the problems of blurred details and unclear visual perception. Therefore, we propose an adaptive weight adjustment super-resolution (AWSR) reconstruction model in this paper. The model includes Shallow Feature Extraction (SFE), Information Extraction Enhancement Block (IDEB) and Reconstruction Block (RB). IDEB composed of Adaptive Weight Blocks (AWB) and Channel Linking Layers (CLL) learns a deeper mapping relationship between LR image and HR image by adaptively adjusting the proportions of different branches. It not only saves computational cost, but also improves the expression ability of the model. Meanwhile, the performance of the model is further improved by dimension change in the up-sample block. Especially, the image edge and texture reconstruction effects are obviously improved. Compared with SRNHARB algorithm proposed in 2021, the PSNR values are increased by 0.23[Formula: see text]dB, 0.19[Formula: see text]dB and 0.02[Formula: see text]dB at [Formula: see text] on the Set5 dataset. Moreover, the proposed model has a strong generalization ability, and the reconstructed SR images can achieve satisfactory results.