HSVNet: Reconstructing HDR Image from a Single Exposure LDR Image with CNN

Abstract

Most photographs are low dynamic range (LDR) images that might not perfectly describe the scene as perceived by humans due to the difference in dynamic ranges between photography and natural scenes. High dynamic range (HDR) images have been used widely to depict the natural scene as accurately as possible. Even though HDR images can be generated by an exposure bracketing method or HDR-supported cameras, most photos are still taken as LDR due to annoyance. In this paper, we propose a method that can produce an HDR image from a single arbitrary exposure LDR image. The proposed method, HSVNet, is a deep learning architecture using a Convolutional Neural Networks (CNN) based U-net. Our model uses the HSV color space that enables the network to identify saturated regions and adaptively focus on crucial components. We generated a paired LDR-HDR image dataset of diverse scenes including under/oversaturated regions for training and testing. We also show the effectiveness of our method through experiments, compared to existing methods.

Keywords

• high dynamic range
• low dynamic range
• HSV color space
• convolutional neural networks
• U-net
• supervised learning