Single Image HDR Synthesis Using a Dense Reverse Connection Network

In order to better improve the image quality of high dynamic range (HDR) scenes, the single-exposure image is used to generate HDR images without increasing the hardware cost. In order to achieve more effective feature learning, a deep learning model based on dense reverse connection network is proposed. Compared with continuously learning redundant features, feature reuse through dense connections is a better way to acquire features. On the basis of dense connections, local feedback and residual connections are added to further improve the information flow, increase the network representation ability, and achieve better performance. During the training process, the high-level features delivered by the residual reverse connection modules of each layer all contain some of the optimal weights and rich local feature information required for image reconstruction. Global reverse connection adds the high-level features of each layer to the input, and uses the high-level features to guide the low-level features to train through the hidden state. Compared with other forward transfer networks, it avoids the loss of optimal results, obtains higher image brightness and contrast, reduces gradient disappearance, and enhances high-frequency details. The model is an end-to-end training network. The experimental results show that the method in this paper has higher SSIM and PSNR indicators than existing methods, and achieves better subjective vision results. The brightness and details of the LDR image are better restored.