Near-Infrared Coloring via a Contrast-Preserving Mapping Model

TL;DR

This paper presents a contrast-preserving mapping model for realistic near-infrared coloring that maintains image contrast and details while transferring colors from visible images, improving image realism and applicability to denoising.

Contribution

It introduces a novel contrast-preserving mapping model for near-infrared coloring that addresses brightness discrepancies and enhances realism compared to naive methods.

Findings

Preserves local contrast and details of near-infrared images.

Successfully transfers realistic colors from visible images.

Applicable to near-infrared denoising tasks.

Abstract

Near-infrared gray images captured together with corresponding visible color images have recently proven useful for image restoration and classification. This paper introduces a new coloring method to add colors to near-infrared gray images based on a contrast-preserving mapping model. A naive coloring method directly adds the colors from the visible color image to the near-infrared gray image; however, this method results in an unrealistic image because of the discrepancies in brightness and image structure between the captured near-infrared gray image and the visible color image. To solve the discrepancy problem, first we present a new contrast-preserving mapping model to create a new near-infrared gray image with a similar appearance in the luminance plane to the visible color image, while preserving the contrast and details of the captured near-infrared gray image. Then based on the proposed contrast-preserving mapping model, we develop a method to derive realistic colors that can be added to the newly created near-infrared gray image. Experimental results show that the proposed method can not only preserve the local contrasts and details of the captured near-infrared gray image, but transfers the realistic colors from the visible color image to the newly created near-infrared gray image. Experimental results also show that the proposed approach can be applied to near-infrared denoising.