CACRNET: COMPRESSION-AWARE CONTEXT ROUTING FOR JPEG ARTIFACT REMOVAL

Blind JPEG image restoration is a practical yet challenging task, as compression artifacts are spatially non-uniform and highly entangled with genuine image structures. Existing methods typically rely on either local restoration or global prompts alone. As a result, heavily degraded regions may receive insufficient contextual compensation, while artifact-contaminated shallow features can still leak into the decoder through skip connections. To address these limitations, we propose a Compression-Aware Context Routing Network, a restoration framework that uses local degradation evidence to coordinate both contextual compensation and skip-feature selection. Specifically, we proposed a Spatial-Channel Interaction (SCI) module derives local degradation cues and uses them to retrieve location-specific contextual prompts. These prompts are drawn from a Contextual Codebook Bank, which recursively aggregates multiscale encoder features into a compact set of image-adaptive tokens, and are injected into the decoder through efficient factorized spatial attention. In parallel, we design a Compression-Aware Gated Fusion (CAGF) module to recalibrate skip features before fusion, suppressing artifacts while preserving reliable structural details. Extensive experiments on blind, non-blind, and double JPEG restoration benchmarks demonstrate that CACRNet achieves superior quantitative performance and faithfully restores structural details across different compression settings.