Deep tone mapping network in HSV color space Tone mapping operators can convert high dynamic range (HDR) images to low dynamic range (LDR) images so that we can enjoy the informative contents of HDR images with LDR devices. However, current stateoftheart tone mapping algorithms mainly focus on the luminance mapping while neglecting the color component. Meanwhile, they often suffer from halo artifacts and overenhancement. In this paper, we propose a tone mapping network (TMNet) in HueSaturationValue (HSV) color space to obtain better luminance and color mapping. We adopt the improved Wasserstein generative adversarial network (WGANGP) as the basic architecture and further introduce several improvements. A meticulously designed loss function is adopted to push tone mapped image to the natural image manifold. Whatxe2x80x99s more, we create a tone mapped image dataset in which the label images are manually adjusted by photographers. Compared with some stateoftheart tone mapping methods, the proposed method can achieve better performance in both subjective and objective evaluations.

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.