Asymmetric Stereo Color Transfer

Abstract

Dual-camera systems containing a color camera and a monochrome camera are widely equipped on smartphones. The color camera captures chrominance information while the monochrome camera captures fine details, which causes asymmetry across spectral and spatial dimensions. In these imaging systems, the chrominance information of low-resolution (LR) color images and the spatial information of high-resolution (HR) monochrome images are highly complementary. In this paper, we propose an elaborate convolutional neural network to recover HR color images by transferring color information from LR color images to HR monochrome images. The network contains a novel feature extraction module named U-ASPP and an asymmetric parallax attention module (APAM). Our network achieves state-of-the-art performance on the Flickr1024 stereo dataset with high efficiency. Moreover, the effectiveness of our trained network is validated in real-world asymmetric image pairs captured by a smartphone, which demonstrates that our method has high generalization capability in real-world imaging systems.