  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 feed-forward 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 feed-forward
network, the reconstruction from LDR to HDR in a feedback network is learned
over multiple iterations. This enables us to create a coarse-to-fine
representation, leading to an improved reconstruction at every iteration.
Various advantages over standard feed-forward networks include early
reconstruction ability and better reconstruction quality with fewer network
parameters. We design a dense feedback block and propose an end-to-end feedback
network- FHDR for HDR image generation from a single exposure LDR image.
Qualitative and quantitative evaluations show the superiority of our approach
over the state-of-the-art methods.
