RethinkingImbalanceinImageSuper-Resolution forEfficientInference

Image super-resolution (SR) aims to reconstruct high-resolution (HR) images with more details from low-resolution (LR) images. Recently, deep learning-based image SR methods have made significant progress inreconstruction performance through deeper networkmodels andlarge-scale training datasets, but these improvements place higher demands on both computing power and memory resources, thus requiring more efficient solutions.