Lightweight Single Image Super-Resolution With Similar Feature Fusion Block

Abstract

Convolutional neural network-based image super-resolution methods have achieved great success in recent years. However, the huge memory and computational costs make most of the existing methods difficult to be applied to resource-constrained scenarios such as edge devices. To tackle this problem, we propose a generic, lightweight and efficient feature fusion block to replace the commonly used 1*1 convolution. In addition, we propose the enhanced shallow residual blocks to improve the super-resolution performance. By combining these two novel blocks, we design an efficient similar feature fusion network for single image super-resolution, based on the observation that cross-layer features of the same channel usually have high similarities. The similar feature fusion block utilizes similarity as a guide for feature clustering, enabling efficient and high-performance cross-layer feature fusion. On the other hand, the enhanced shallow residual blocks are used as the base feature extraction model for the network to improve super-resolution performance in conjunction with the feature fusion module. In the enhanced shallow residual blocks, we combine convolution with identity connection to maintain the similarity of cross-layer features that are fed into the similar feature fusion block. The spatial attention mechanism is also introduced to reinforce the useful spatial features. Experimental results on the benchmark datasets show that the proposed method can achieve comparable results to state-of-the-art methods with a small number of parameters.