Lightweight but Global: Event-Guided Broad-Range Brightness Restoration

Broad-range brightness restoration maps RGB frames captured under low, normal, or high illumination to a target exposure. Events provide complementary measurements when the frames are noisy or saturated. Balancing global spatial context, temporal information, and reconstruction detail within a compact model remains challenging. To this end, we present MambaSEE, a compact spatiotemporal architecture that assigns these functions to different operators. First, fixed-width additive fusion and attentive state-space modules model each frame–event pair with global context. Next, a weight-and-activation 8-bit quantization-aware ConvGRU aggregates three first-in, first-out pairs through backward-then-forward propagation, without observations later than the prediction time. Finally, a prompt-conditioned depthwise decoder reconstructs fine image details. On the SDE and SEE-600K benchmarks, MambaSEE achieves the highest reported average PSNR with the lowest FP32-equivalent weight storage among the compared methods. On SEE-600K, its PSNR values are 2.01, 1.39, and 5.32 dB higher than the published SEENet results for low-, high-, and normal-light inputs, respectively. MambaSEE uses mixed-precision weight storage equivalent to 0.48M FP32 parameters, about one quarter of SEENet's.