FHDR: HDR Image Reconstruction from a Single LDR Image using Feedback Network High dynamic range (HDR) image generation from a single exposure low dynamic range (LDR) image has been made possible due to the recent advances in Deep Learning. Various feedforward Convolutional Neural Networks (CNNs) have been proposed for learning LDR to HDR representations. To better utilize the power of CNNs, we exploit the idea of feedback, where the initial low level features are guided by the high level features using a hidden state of a Recurrent Neural Network. Unlike a single forward pass in a conventional feedforward network, the reconstruction from LDR to HDR in a feedback network is learned over multiple iterations. This enables us to create a coarsetofine representation, leading to an improved reconstruction at every iteration. Various advantages over standard feedforward networks include early reconstruction ability and better reconstruction quality with fewer network parameters. We design a dense feedback block and propose an endtoend feedback networkFHDR for HDR image generation from a single exposure LDR image. Qualitative and quantitative evaluations show the superiority of our approach over the stateoftheart methods.

DualStreams Global Guided Learning for High Dynamic Range Image Reconstruction High dynamic range (HDR) images capture the luminance information of the real world and have more detailed information than low dynamic range (LDR) images. In this paper, we propose a dualstreams global guided endtoend learning method to reconstruct HDR image from a single LDR input that combines both global information and local image features. In our framework, global features and local features are separately learned in dualstreams branches. In the reconstructed phase, we use a fusion layer to fuse them so that the global features can guide the local features to better reconstruct the HDR image. Furthermore, we design mixed loss function including multiscale pixelwise loss, color similarity loss and gradient loss to jointly train our network. Comparative experiments are carried out with other stateoftheart methods and our method achieves superior performance.