Pixel Ignores, Superpixel Sees: Adverse Weather Image Restoration via Semantic-Center SSM

Adverse weather image restoration aims to recover clear vis-ibility from degraded images in complex weather conditions. Existingworks attempt to address this problem by modeling relationships betweenpixels, however, this paradigm defies the spatially non-uniformity factof degradations and learns non-discriminative features from semantic-conflict regions. In this paper, we propose SSR, a Semantic-center guildedState space model for image Restoration. The key idea of SSR is to shiftthe conventional scanning strategy of pixel-serial to semantic-guildedone. Specifically, we introduce a Superpixel-guided Selective Scan Mech-anism (S3 M), which first partitions the image into perceptually coher-ent regions via superpixel clustering and then performs relations mod-eling within the semantic-related regions. Moreover, a Region-level Gat-ing Mechanism (RGM) is developed to perform intra-region calibra-tion by modulating degradation outliers within each semantic super-pixel unit along the channel dimension. Extensive experiments on 6well-established benchmarks demonstrate that SSR performs favorablyagainst state-of-the-art models with competitive computational cost. Thesource code is publicly available at https://github.com/LIDAYU-DayuLi/SSR.