Contrast Enhancement and Image Completion: A CNN Based Model to Restore Ill Exposed Images Digital cameras work through transforming the scenexe2x80x99s radiance into an electrical charge. Optical arrangement, sensors, and embedded electronics often limit the accuracy of the representation. Scenes with a dynamic range above the capability of the camera or poor lighting are challenging conditions, which usually result in low contrast images. Soft clipping is usually compensated by transforming the power and shifting the imagexe2x80x99s histogram. However, under extreme conditions, ill exposure results in severe clipping that requires interpolation and painting. We introduce a model of convolutional neural network to perform signal reconstruction and interpolation. It is designed to be used on sRGB images. The results are evaluated using several metrics of image quality that indicate that the proposed network can improve images that are damaged by different conditions of exposure. In addition, our method offers a substantial gain over stateoftheart methods.

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.