Deep tone mapping network in HSV color space

Ning Zhang, Chao Wang, Yang Zhao, Ronggang Wang

Abstract: 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 state-of-the-art tone mapping algorithms mainly focus on the luminance mapping while neglecting the color component. Meanwhile, they often suffer from halo artifacts and over-enhancement. In this paper, we propose a tone mapping network (TMNet) in Hue-Saturation-Value (HSV) color space to obtain better luminance and color mapping. We adopt the improved Wasserstein generative adversarial network (WGAN-GP) 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. What’s more, we create a tone mapped image dataset in which the label images are manually adjusted by photographers. Compared with some state-of-the-art tone mapping methods, the proposed method can achieve better performance in both subjective and objective evaluations.