Scan Clusters, Not Pixels: A Cluster-Centric Paradigm for Efficient Ultra-high-definition Image Restoration

TL;DR

This paper introduces C²SSM, a cluster-centric state space model that significantly improves the efficiency of UHD image restoration by processing image clusters instead of individual pixels, achieving state-of-the-art results.

Contribution

The paper proposes a novel cluster-centric paradigm for UHD image restoration, shifting from pixel-wise to cluster-wise processing to enhance efficiency and scalability.

Findings

Achieves state-of-the-art results across five UHD restoration tasks.

Reduces computational costs significantly compared to pixel-based models.

Introduces a dual-path process combining cluster reasoning and pixel detail preservation.

Abstract

Ultra-High-Definition (UHD) image restoration is trapped in a scalability crisis: existing models, bound to pixel-wise operations, demand unsustainable computation. While state space models (SSMs) like Mamba promise linear complexity, their pixel-serial scanning remains a fundamental bottleneck for the millions of pixels in UHD content. We ask: must we process every pixel to understand the image? This paper introduces C$^2$SSM, a visual state space model that breaks this taboo by shifting from pixel-serial to cluster-serial scanning. Our core discovery is that the rich feature distribution of a UHD image can be distilled into a sparse set of semantic centroids via a neural-parameterized mixture model. C$^2$SSM leverages this to reformulate global modeling into a novel dual-path process: it scans and reasons over a handful of cluster centers, then diffuses the global context back to all pixels through a principled similarity distribution, all while a lightweight modulator preserves fine details. This cluster-centric paradigm achieves a decisive leap in efficiency, slashing computational costs while establishing new state-of-the-art results across five UHD restoration tasks. More than a solution, C$^2$SSM charts a new course for efficient large-scale vision: scan clusters, not pixels.