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. Then we propose the universality assessment equation (UAE), a metric which quantifies how readily a given module could be transplanted across models and reveals the combined influence of multiple existing metrics on transferability. 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 and other low-level tasks, 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 0.83 dB or enabling a 71.3% reduction in parameters with negligible loss in reconstruction fidelity. Similar optimization approaches could be applied to a broader range of basic modules, offering a new paradigm for the design of plug-and-play modules.