Enhanced Lightweight Image Super-Resolution via Residual Aggregation and Wavelet Loss

Image super-resolution (SR), which aims to reconstruct a high-resolution image from a low-resolution input, has progressed from convolutional neural networks (CNNs) to transformer-based architectures. Despite this progress, lightweight transformer SR remains challenging: local or window-based operations provide limited long-range interaction, conventional query-key-value projections introduce parameter and computational redundancy, and pixel-domain loss alone provides insufficient frequency-domain constraints on fine structures. This study presents RAW, a lightweight SR network based on residual aggregation and wavelet loss. RAW uses local aggregation to preserve neighborhood textures, mesoscale grouped-residual attention to reduce projection redundancy while modeling regional dependencies, and non-local sparse aggregation to capture long-range information at a controlled cost. By integrating stationary-wavelet-transform loss with RGB-domain L1 loss, the model supervises structural and high-frequency information without adding an inference branch. Experiments on standard benchmarks demonstrate a competitive trade-off between reconstruction quality and computational complexity. For 4× SR, RAW reduces the numbers of parameters and MACs by 14.3% and 15.4%, respectively, relative to the baseline, while improving the PSNR and SSIM on Manga109 by 0.22 dB and 0.0015, respectively.