MineSSM: Frequency-Decoupled State-Space Modeling for Real-Time Low-Light Enhancement in Dusty Underground Mines

Safety monitoring and visual surveillance in underground coal mines must cope with extreme low light and pervasive coal dust, yet the most accurate low-light image enhancement models rely on self-attention, whose quadratic cost precludes real-time use on the edge-computing platforms deployed underground, such as inspection robots and explosion-proof cameras. Our key observation is that these two degradations are separable by a wavelet decomposition: the low-frequency band carries the illumination to be corrected, whereas dust-induced noise is largely confined to the high-frequency bands. MineSSM turns this observation into an efficient design that routes each band to the operator it needs: a linear-complexity state-space model (Mamba) that homogenizes the global illumination and a lightweight convolutional branch that performs dust suppression on the high-frequency bands, so that no heavy operator ever runs at full resolution, and the two branches are recomposed under a Retinex constraint for faithful color restoration. A frequency-domain analysis of a real dusty mining frame confirms this split, with the smooth veil and lamp glow in the low-frequency band and the discrete dust speckles in the high-frequency bands. MineSSM surpasses the Transformer-based MEFormer on the MELOL mining dataset (26.58 dB PSNR/0.96 SSIM vs. 26.34 dB/0.91 SSIM) and attains the best PSNR and SSIM on the LOLv1 benchmark, while running at 27.8 FPS (0.036 s per 400×600 image) on a desktop GPU with linear resolution scaling. On an embedded NVIDIA Jetson Orin NX it sustains 8.0 FPS at 400×600, supporting near-real-time on-device enhancement at typical surveillance resolutions.