Pixel Ignores, Superpixel Sees: Adverse Weather Image Restoration via Semantic-Center SSM
2026-08-03 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors propose a new method called SSR to fix blurry or unclear images caused by bad weather. Instead of looking at each pixel one by one, their approach groups parts of the image that share similar features (called superpixels) and processes these regions together. This helps the model better understand and correct different parts of the image separately. They also include a step to adjust unusual areas within each region for better restoration. Their tests show that SSR works well compared to other methods and does this efficiently.
Image RestorationAdverse WeatherSuperpixelSemantic SegmentationState Space ModelDegradationSemantic-guided ProcessingRegion-level GatingComputational Efficiency
Authors
Dayu Li, Shihao Zhou, Leizhi Shu, Jin Wu, Chi Man Vong, Jufeng Yang
Abstract
Adverse weather image restoration aims to recover clear visibility from degraded images in complex weather conditions. Existing works attempt to address this problem by modeling relationships between pixels, however, this paradigm defies the spatially non-uniformity fact of degradations and learns non-discriminative features from semantic-conflict regions. In this paper, we propose SSR, a \textbf{S}emantic-center guilded \textbf{S}tate space model for image \textbf{R}estoration. The key idea of SSR is to shift the conventional scanning strategy of pixel-serial to semantic-guilded one. Specifically, we introduce a Superpixel-guided Selective Scan Mechanism ($\text{S}^3$M), which first partitions the image into perceptually coherent regions via superpixel clustering and then performs relations modeling within the semantic-related regions. Moreover, a Region-level Gating Mechanism (RGM) is developed to perform intra-region calibration by modulating degradation outliers within each semantic superpixel unit along the channel dimension. Extensive experiments on \textbf{6} well-established benchmarks demonstrate that SSR performs favorably against state-of-the-art models with competitive computational cost.