New Stereo High Dynamic Range Imaging Method Using Generative Adversarial Networks

Abstract

Stereo high dynamic range (HDR) image/video can be generated by using a pair of stereo cameras with different exposure parameters. This paper proposes a new stereo HDR imaging method using generative adversarial networks (GAN) with a low dynamic range (LDR) stereo imaging system. It is assumed here that the left-view (LV) image is under-exposed and the right-view (RV) image is overexposed. First, a view exposure transfer GAN (VET-GAN) is constructed to transfer exposure information of the RV image to the LV image to generate the multi-exposure LV images, and then an HDR fusion GAN is constructed to fuse the generated multi-exposure LV images into an LV HDR image. Similarly, an RV HDR image can be generated using the same way to form a stereo HDR image pair. The experimental results show that the proposed method can obtain stereo HDR images with high visual quality and effectively avoid the ghost artifacts caused by parallax.