Guided Colorization Using Mono-Color Image Pairs

TL;DR

This paper presents a novel mono-color image enhancement method that uses dual-camera pairs to improve colorization quality by dense scribbling, outlier removal, and seed-based propagation, resulting in higher SNR and richer details.

Contribution

It introduces a new mono-color image colorization algorithm leveraging dense scribbling, outlier detection, and seed generation to enhance visual quality and reduce color bleeding.

Findings

Restores high-quality color images from mono-color pairs with improved SNR.

Effectively removes outliers like occlusion and color ambiguity.

Achieves better color propagation and detail preservation.

Abstract

Compared to color images captured by conventional RGB cameras, monochrome images usually have better signal-to-noise ratio (SNR) and richer textures due to its higher quantum efficiency. It is thus natural to apply a mono-color dual-camera system to restore color images with higher visual quality. In this paper, we propose a mono-color image enhancement algorithm that colorizes the monochrome image with the color one. Based on the assumption that adjacent structures with similar luminance values are likely to have similar colors, we first perform dense scribbling to assign colors to the monochrome pixels through block matching. Two types of outliers, including occlusion and color ambiguity, are detected and removed from the initial scribbles. We also introduce a sampling strategy to accelerate the scribbling process. Then, the dense scribbles are propagated to the entire image. To alleviate incorrect color propagation in the regions that have no color hints at all, we generate extra color seeds based on the existed scribbles to guide the propagation process. Experimental results show that, our algorithm can efficiently restore color images with higher SNR and richer details from the mono-color image pairs, and achieves good performance in solving the color bleeding problem.