Abstract
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.
Highlights
In low-light environments, an image that is captured by a single RGB color camera using a Bayer color filter array (CFA) usually suffers from high noise and low detail information due to low-quantum efficiency
The Middlebury dataset consists of a set of multi-view color images that were captured from multiple RGB cameras for a scene in various illuminance and exposure conditions
For the dual camera simulation, the authors assumed that the left-view was obtained from the color camera and the right-view was obtained from the mono camera
Summary
In low-light environments, an image that is captured by a single RGB color camera using a Bayer color filter array (CFA) usually suffers from high noise and low detail information due to low-quantum efficiency. Various studies have been conducted to enhance images captured under low-light conditions to overcome these shortfalls of Bayer color cameras. It is known that a monochrome camera can obtain images with less noise and improved detail visibility because of the absence of a color filter array [1]. An image fusion technique is used to apply these features of a monochrome camera to a corresponding noisy color image. The image fusion technique for a dual camera approach includes two processing steps: per-pixel registration and two image combination. The color- and mono-image pair can be combined by appropriate fusion rules to obtain a visually pleasurable output image
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