# WaveHiT-SR: Hierarchical Wavelet Network for Efficient Image Super-Resolution

## TL;DR

WaveHiT-SR introduces a hierarchical wavelet transformer that efficiently captures multi-scale features for image super-resolution, outperforming existing methods in accuracy and computational efficiency.

## Contribution

The paper presents a novel hierarchical transformer framework embedding wavelet transforms and adaptive windows, enabling better long-range dependency modeling and multi-frequency feature extraction in SR.

## Key findings

- Achieves state-of-the-art super-resolution results.
- Reduces computational complexity while maintaining performance.
- Fewer parameters and faster inference speeds.

## Abstract

Transformers have demonstrated promising performance in computer vision tasks, including image super-resolution (SR). The quadratic computational complexity of window self-attention mechanisms in many transformer-based SR methods forces the use of small, fixed windows, limiting the receptive field. In this paper, we propose a new approach by embedding the wavelet transform within a hierarchical transformer framework, called (WaveHiT-SR). First, using adaptive hierarchical windows instead of static small windows allows to capture features across different levels and greatly improve the ability to model long-range dependencies. Secondly, the proposed model utilizes wavelet transforms to decompose images into multiple frequency subbands, allowing the network to focus on both global and local features while preserving structural details. By progressively reconstructing high-resolution images through hierarchical processing, the network reduces computational complexity without sacrificing performance. The multi-level decomposition strategy enables the network to capture fine-grained information in lowfrequency components while enhancing high-frequency textures. Through extensive experimentation, we confirm the effectiveness and efficiency of our WaveHiT-SR. Our refined versions of SwinIR-Light, SwinIR-NG, and SRFormer-Light deliver cutting-edge SR results, achieving higher efficiency with fewer parameters, lower FLOPs, and faster speeds.