Exploiting Long and Short Temporal Dependence for Low-Light Video Enhancement

Existing learning-based methods often lack temporal coherence in low-light video enhancement due to rarely considering intrinsic temporal dependence. To address this issue, we propose the Long-short Temporal Filtering Network (TFNet) to learn the mapping from low-light videos to normal-light ones, utilizing the well-considered data-centric strategy and a refined architecture. From the data-centric temporal strategy, we incorporate both long-range and short-range temporal dependence into TFNet, effectively capturing the temporal information. From the model design perspective, the TFNet incorporates the Temporal-aware Attentional Filtering (TAF) module, which aims to estimate and adaptively combine filtering kernels for guided filtering towards features of the middle frame. To further refine the filtered features, the cascaded Grouped Attention (GA) blocks are presented in a grouped attention strategy. Experimental results on benchmark datasets have demonstrated the superiority of our TFNet against the state-of-the-art methods in terms of video frame quality and brightness consistency.