A few approaches have studied image fusion using color-plus-mono dual cameras to improve the image quality in low-light shooting. Among them, the color transfer approach, which transfers the color information of a color image to a mono image, is considered to be promising for obtaining improved images with less noise and more detail. However, the color transfer algorithms rely heavily on appropriate color hints from a given color image. Unreliable color hints caused by errors in stereo matching of a color-plus-mono image pair can generate various visual artifacts in the final fused image. This study proposes a novel color transfer method that seeks reliable color hints from a color image and colorizes a corresponding mono image with reliable color hints that are based on a deep learning model. Specifically, a color-hint-based mask generation algorithm is developed to obtain reliable color hints. It removes unreliable color pixels using a reliability map computed by the binocular just-noticeable-difference model. In addition, a deep colorization network that utilizes structural information is proposed for solving the color bleeding artifact problem. The experimental results demonstrate that the proposed method provides better results than the existing image fusion algorithms for dual cameras.
Keywords: color transfer; convolutional neural network (CNN); dual camera; low-light enhancement.