High dynamic range (HDR) image generation from a single exposure low dynamic\nrange (LDR) image has been made possible due to the recent advances in Deep\nLearning. Various feed-forward Convolutional Neural Networks (CNNs) have been\nproposed for learning LDR to HDR representations. To better utilize the power\nof CNNs, we exploit the idea of feedback, where the initial low level features\nare guided by the high level features using a hidden state of a Recurrent\nNeural Network. Unlike a single forward pass in a conventional feed-forward\nnetwork, the reconstruction from LDR to HDR in a feedback network is learned\nover multiple iterations. This enables us to create a coarse-to-fine\nrepresentation, leading to an improved reconstruction at every iteration.\nVarious advantages over standard feed-forward networks include early\nreconstruction ability and better reconstruction quality with fewer network\nparameters. We design a dense feedback block and propose an end-to-end feedback\nnetwork- FHDR for HDR image generation from a single exposure LDR image.\nQualitative and quantitative evaluations show the superiority of our approach\nover the state-of-the-art methods.\n