Perceptually Fractural Pixel Values in Rendering High Dynamic Range Images High Dynamic Range (HDR) images capture the full range of luminance present in real world scenes, and unlike Low Dynamic Range (LDR) images, can simultaneously contain detailed information in the deepest of shadows and the brightest of light sources. In order to render HDR image on LDR displayers, it is often necessary to create LDR depictions of HDR images at the cost of contrast information loss. To reduce the loss, this paper enables to render HDRI (High Dynamic Range Image) with multiple lowbit images periodically. From the viewpoint of a human, the pixel value is fractural. It does not adjust the tones but can reconstruct HDR images.

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.