iTM-Net: Deep Inverse Tone Mapping Using Novel Loss Function Considering Tone Mapping Operator
Yuma Kinoshita, Hitoshi Kiya · IEEE Access · 2019
In this paper, we propose a novel inverse tone mapping network, called “iTM-Net.” For training iTM-Net, we also propose a novel loss function that considers the non-linear relation between low dynamic range (LDR) and high dynamic range (HDR) images. For inverse tone mapping with convolutional neural networks (CNNs), we first point out that training CNNs with a standard loss function causes a problem due to the non-linear relation between the LDR and HDR images. To overcome the problem, the novel loss function non-linearly tone-maps target HDR images into LDR ones on the basis of a tone mapping operator, and the distance between the tone-mapped images and predicted ones are then calculated. The proposed loss function enables us not only to normalize the HDR images but also to reduce the non-linear relation between LDR and HDR ones. The experimental results show that the HDR images predicted by the proposed iTM-Net have higher-quality than the HDR ones predicted by conventional inverse tone mapping methods, including the state of the art, in terms of both HDR-VDP-2.2 and PU encoding + MS-SSIM. In addition, compared with loss functions that do not consider the non-linear relation, the proposed loss function is shown to improve the performance of CNNs.