Difficulty-Aware Dynamic Network for Lightweight Exposure Correction

Recently, deep learning-based methods have been successfully applied to the field of exposure correction. However, most of the existing methods treat different locations of an image in the same way, ignoring the inhomogeneous recovery difficulty and spatially-varying visual patterns in the image, which is sub-optimal and not perfectly efficient. In this paper, we propose a difficulty-aware dynamic network (DDNet) for lightweight exposure correction. Specifically, we propose a difficulty-aware strategy that determines the difficulty of feature patches according to a difficulty mask. Then, only the difficult patches are further refined instead of the whole features, which greatly reduces the overall computational complexity. Moreover, in order to achieve spatially-varying processing with a minimal computational burden, we design a spatial-aware dynamic convolution (SDConv), which is generated by predicting a set of basic kernels and a spatial-aware weight map. Benefiting from these designs, our method can strike a good trade-off between performance and complexity. Extensive experiments on several datasets demonstrate that our approach outperforms the state-of-the-art methods both qualitatively and quantitatively while requiring cheaper computational costs.