Effective Local Texture Estimation Using Wavelet Transforms for Arbitrary-Scale Super-Resolution

Image super-resolution SR aims to reconstruct high-resolution images from low-resolution inputs, addressing challenges like sensor noise, optical distortions, and compression artifacts. Traditional SR methods often struggle with preserving fine details, particularly in regions with sharp transitions or complex textures. In this work, we propose a novel Local Wavelet Transformer LWT framework that leverages the Discrete Wavelet Transform DWT to capture both local textures and global structures, improving the accuracy of fine-grained detail restoration. By introducing a magnification factor decomposition strategy, our method enables super-resolution at arbitrary scaling levels, ensuring flexibility and precise detail preservation across different magnifications. We demonstrate the effectiveness of our approach through extensive experiments on multiple benchmark datasets, showing superior performance and achieving state-of-the-art results in high-resolution image reconstruction under diverse conditions. Our results highlight the potential of wavelet-based analysis for enhancing SR tasks, particularly in scenarios requiring fine detail recovery and sharp transitions.