Near-Infrared Coloring via a Contrast-Preserving Mapping Model
This is an incremental improvement for image processing tasks like restoration and classification, enhancing realism in colored near-infrared images.
The paper tackles the problem of coloring near-infrared gray images by introducing a contrast-preserving mapping model to address discrepancies in brightness and structure with visible color images, resulting in realistic color transfer and application to near-infrared denoising.
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.