Fidelity- and Perception-Aware Local Implicit Attention for Arbitrary-Scale Image Super-Resolution

Arbitrary-scale image super-resolution (ASISR) aims to recon-struct high-resolution images from low-resolution inputs over a continuousrange of upscaling factors. While traditional pixel-regression approachesoften produce overly smooth results that lack realistic details, recent dif-fusion methods can produce sharper and more realistic textures. However,these diffusion techniques frequently introduce the risk of structural hal-lucinations. To address these issues, we propose Fidelity- and Perception-Aware Local Implicit Attention (FPLIA), a framework that effectivelyintegrates fidelity-oriented features into a diffusion pipeline to produce re-alistic and faithful reconstructions for ASISR. We introduce a Fidelity andPerception Attention Module (FPAM), which applies both self-attentionand cross-attention to fidelity-oriented and perceptual features to en-hance representational capacity. To further exploit their complements,we design a Fidelity and Perception Select Module (FPSM) that adap-tively selects the most representative features for RGB values prediction.We conduct extensive experiments to validate the effectiveness of thesecomponents. Both qualitative and quantitative results show that FPLIAdelivers superior perceptual realism while maintaining reconstructionaccuracy on standard ASISR benchmarks. The source code is accessibleat the following repository: https://github.com/XUSean0118/FPLIA.