Photo-realistic image bit-depth enhancement via residual transposed convolutional neural network

Nowadays, with the rapid development of high bit-depth (HBD) monitors, the demands for high quality image visualization have been raised. However, a prominent problem is the inconsistency between existing low bit-depth (LBD) images and HBD monitors. When LBD images are simply de-quantized to HBD ones, there will be severe false contour artifacts in smooth gradient areas, degrading image visual quality. Therefore, bit-depth enhancement plays a key role in viewing LBD images on HBD monitors. In this paper, motivated by the promising results of deep Convolutional Neural Network (CNN) in generating realistic high-quality images, we proposed a novel algorithm to recover photo-realistic HBD images. To the best of our knowledge, CNN is introduced to bit-depth enhancement task for the first time. A novel neural network is proposed with summation and concatenation skip connections among transposed convolutional layers to cope with the gradient vanishing problem. Besides, different from traditional pixel-wise loss functions, perceptual loss is adopted to reconstruct images with higher visual quality and structural similarity to original HBD sources. Experiments performed on three datasets demonstrate that the proposed method outperforms state-of-the-art algorithms objectively and subjectively with suppressed false contour artifacts and preserved textures. (C) 2019 Elsevier B.V. All rights reserved.