Residual dense U-Net for abnormal exposure restoration from single images

Digital imaging devices sometimes capture images with abnormal exposure because of the complex lighting conditions and limited dynamic range of luminance. In this work, a new residual dense U-Net is proposed to predict the information that has been lost in saturated image areas, to enable abnormal exposure restoration from a single image. Full advantage of the multi-level features is taken from all the convolution layers in the restoration process. Specifically, the densely connected convolutional layers are used in a contracting encoder net to extract abundant local features. The transition layer and local residual learning after each dense block is then applied to adaptively learn more effectively from prior with present local features. Further, an expanding decoder net with dense layers is used and added with skip connections to preserve low-level information and existing details. Finally, multiple global residual learning is used to adaptively extract hierarchical features and help train the network. It is shown that such a network can be trained end-to-end from abnormal exposure images and outperform the prior best method on image enhancement. Experimental results show that the proposed model can greatly enhance the dynamic range of an abnormal exposure image.