TRAN: Texture Response Adjustment Network for Lightweight Single-Image Super-Resolution

Abstract

Single Image Super-Resolution (SISR) aims to reconstruct a high-resolution (HR) image from a given low-resolution (LR) input. Reconstruction difficulty varies across textures of different complexity. However, existing methods apply a single, unified feature modeling and reconstruction pipeline for all textures. This often exacerbates the reconstruction imbalance across textures of varying complexity. To address this issue, this study proposes Texture Response Adjustment Network for SISR (TRAN). This method achieves more balanced fine-grained reconstruction and improves overall reconstruction quality by adaptively adjusting texture response. TRAN comprises two main components. The first mitigates local texture response bias within an image by decoupling textures of different complexity in the frequency domain for independent modeling and response adjustment. The second alleviates global texture response bias across different images by dynamically perceiving overall image complexity and adjusting the network structure accordingly. Experiments on multiple benchmark datasets demonstrate that the proposed TRAN balances fine-grained reconstruction across textures of different complexity and improve overall reconstruction performance. Notably, on the $\times 4$ SISR task of the Manga109 dataset, TRAN outperforms MambaIRv2-L by 0.31 dB in PSNR while reducing FLOPs by 38.1G.