Illuminant Aware Gamut‐Based Color Transfer

Abstract

A) Source image (B) Target image (C) Our method (D) Reinhard et al. [2001] (E) Pitie et al. [2007] (F) Xiao and Ma [2009] Figure 1: This figure compares color transfer results of several methods. Our method incorporates information about the source and target scene illuminants and constrains the color transfer to lie within the color gamut of the target image. Our resulting image has a more natural look and feel than existing methods. Abstract This paper proposes a new approach for color transfer between two images. Our method is unique in its consideration of the scene illumination and the constraint that the mapped image must be within the color gamut of the target image. Specifically, our approach first performs a white-balance step on both images to remove color casts caused by different illuminations in the source and target image. We then align each image to share the same 'white axis' and perform a gradient preserving histogram matching technique along this axis to match the tone distribution between the two images. We show that this illuminant-aware strategy gives a better result than directly working with the original source and target image's luminance channel as done by many previous methods. Afterwards, our method performs a full gamut-based mapping technique rather than processing each channel separately. This guarantees that the colors of our transferred image lie within the target gamut. Our experimental results show that this combined illuminant-aware and gamut-based strategy produces more compelling results than previous methods. We detail our approach and demonstrate its effectiveness on a number of examples.

3D color homography model for photo-realistic color transfer re-coding

“…In the experiments which follow, we call our method-3D homography color transform + mean intensity mapping-"3D-H." Similarly, the 2D homography approach for color transfer re-coding [11] is denoted as "2D-H." We first show some visual results of color transfer approximations of [16,20,21,23] in Fig. 4.…”

“…The "target" image was used by the "original color transfer" algorithms to produce the "original color transfer" output image, but it is not used by the proposed color transfer re-coding algorithm. The orange dashed line divides the pipeline into two steps: (1) color space mapping: The RGBs of the source image (drawn as red edge circles) and the original color transfer image (by [16] with the target image as the reference, black edge circles) are matched according to their locations (e.g., the blue matching lines), from which we estimate a 3D homography matrix H and use H to transfer the source image colors and (2) mean intensity mapping: The image mean intensity values (mean values of R, G, and B) are aligned by estimating the per-pixel shading between the color space-mapped result and the original color transfer result by least squares. The final result is a visually close color transfer approximation demonstrate three applications of the proposed method for color transfer artifact fixing, color transfer acceleration, and color-robust image stitching.…”

“…Their algorithm also handles the difference in dynamic ranges between two images. Nguyen et al [16] proposed an illuminant-aware and gamut-based color transfer. They first eliminate the color cast difference by a whitebalancing operation for both images.…”

“…In our experiment, we choose two computationally expensive methods [16,20] as the inputs and we compare our performance (MATLAB implementation) with a state-ofthe-art method TR [10] (Python implementation). Figure 5 shows the output comparisons between the original color transfer results and the acceleration results.…”

“…Their method proposed that the mean and variance of the source image, in a specially chosen color space, should be manipulated to B Han Gong h.gong@uea.ac.uk, gong@fedoraproject.org 1 School of Computing Sciences, University of East Anglia, Norwich, UK 2 School of Informatics, University of Edinburgh, Edinburgh, UK match those of a target. More recent methods [1,16,[19][20][21] might adopt more aggressive color transfers-e.g., color distribution force matches [19,20]-and yet, these aggressive changes often do not preserve the original intensity gradients and new spatial type artifacts may be introduced into an image (e.g., JPEG blocks become visible or there is false contouring). In addition, the complexity of a color transfer method usually leads to longer processing time.…”