Optimization of Module Transferability in Single Image Super-Resolution: Universality Assessment and Cycle Residual Blocks

Deep learning has substantially advanced the Single Image Super-Resolution (SISR). However, existing researches have predominantly focused on raw performance gains, with little attention paid to quantifying the transferability of architectural components. In this paper, we introduce the concept of "Universality" and its associated definitions which extend the traditional notion of "Generalization" to encompass the modules' ease of transferability, thus revealing the relationships between module universality and model generalizability. Then we propose the Universality Assessment Equation (UAE), a metric for quantifying how readily a given module could be transplanted across models. Guided by the UAE results of standard residual blocks and other plug-and-play modules, we further design two optimized modules, Cycle Residual Block (CRB) and Depth-Wise Cycle Residual Block (DCRB). Through comprehensive experiments on natural-scene benchmarks, remote-sensing datasets, extreme-industrial imagery and on-device deployments, we demonstrate that networks embedded with the proposed plug-and-play modules outperform several state-of-the-arts, reaching a PSNR enhancement of up to 0.83dB or enabling a 71.3% reduction in parameters with negligible loss in reconstruction fidelity.