Multi-Directional Transformer Image Super-Resolution Network Based on Information Enhancement

Abstract

With the advancement of deep learning, single-image super-resolution (SISR) has achieved significant progress. Recently, vision transformer-based super-resolution models have demonstrated remarkable performance; however, their high computational cost hinders their practical application. In this paper, we introduce a lightweight transformer-based super-resolution model termed information-enhanced efficient multi-directional transformer(IEMT). The model employs a dual-branch architecture that integrates the strengths of both convolutional neural network (CNN) and transformer networks. The proposed high-frequency extraction block (HEB) effectively captures high-frequency information from the enhanced image. Furthermore, a multi-directional attention mechanism is incorporated into the transformer branch to comprehensively learn latent features and details, thereby enhancing reconstruction quality. For attention computation, we propose a dynamic parameter-sharing mechanism that adaptively adjusts parameter sharing based on local image features, significantly reducing the model's parameter count. Experimental results demonstrate that the proposed IEMT achieves superior performance on five benchmark datasets, with a significantly reduced parameter count, computational complexity, and memory usage.