HiLiteMamba: A Lightweight and High-Frequency Aware Network for Single Image Super-Resolution

Abstract

Transformer has widely been applied in various low-vision tasks, achieving significant strides in single image super-resolution. However, its low-pass characteristic still limits the ability of Transformer-based models to represent rich texture details in images. Additionally, the quadratic computational complexity of the attention mechanism also restricts its application in low-level tasks. In these regards, we propose a novel High-frequency awareness and Lightweight Mamba network (HiLiteMamba). It combines a lightweight Mamba module with a convolution block to model different features complementarily. Moreover, we develop the High-Freq Highway with residual connection to maintain deep propagation of high-frequency information and accelerate its modeling. Extensive experiments demonstrate that HiLiteMamba achieves state-of-the-art performance among baseline models.