CASSM: Content-Aware State Space Modeling for Ultra-High-Definition Low-Light Image Enhancement

Mamba-based architectures have shown promising results in low-light image enhancement by modeling visual tokens through selective, linear-complexity scanning. However, most existing approaches rely on predefined spatial scanning strategies that determine token order mainly based on spatial locations, ignoring content similarity among visual tokens. Consequently, content-similar yet spatially distant tokens may be placed far apart in the scanning sequence, limiting effective long-range dependency modeling, particularly for ultra-high-definition (UHD) images with millions of pixels. To address this limitation, we propose a Content-Aware State Space Model (CASSM) for UHD low-light image enhancement, which constructs content-aware visual sequences with linear complexity. Specifically, CASSM leverages a learned low-frequency codebook and a lightweight feature alignment adapter to efficiently generate a content-aware assignment map. Guided by these assignments, the Content-Aware Mamba Block performs token-level content grouping to gather content-similar tokens, followed by group-level reordering using 1D t-SNE projections of the codebook entries. This hierarchical organization brings spatially distant yet visually similar tokens closer in the resulting sequence, enabling more effective state-space modeling. Extensive experiments demonstrate that CASSM achieves an average PSNR improvement of 0.75 dB over the state-of-the-art methods across four benchmarks.