Palette-Based Image Recoloring Using Color Decomposition Optimization

Previous works on palette-based color manipulation typically fail to produce visually pleasing results with vivid color and natural appearance. In this paper, we present an approach to edit colors of an image by adjusting a compact color palette. Different from existing methods that fail to preserve inherent color characteristics residing in the source image, we propose a color decomposition optimization for flexible recoloring while retaining these characteristics. For an input image, we first employ a variant of the k -means algorithm to create a palette consisting of a small set of most representative colors. Next, we propose a color decomposition optimization to decompose colors of the entire image into linear combinations of basis colors in the palette. The captured linear relationships then allow us to recolor the image by recombining the coding coefficients with a user-modified palette. Qualitative comparisons with existing methods show that our approach can more effectively recolor images. Further user study quantitatively demonstrates that our method is a good candidate for color manipulation tasks. In addition, we showcase some applications enabled by our method, including pattern colorings suggesting, color transfer, tissue staining analysis and color image segmentation.

I. Introduction

Manipulating colors in photographs is an important problem with many applications in computer vision and image processing. In the past decades, there have been numerous works tackling this problem from different perspectives. Some approaches make complex color modifications possible by using an example image [1], [2], or propagating sparse user edits [3]–[5]. Others automatically adjust colors by leveraging color dataset [6] or using a semantic word depicting the desired style [7]. A recent work of Chang et al. [8] offers an appealing palette-based color manipulation tool, which unifies ease of use and range of expressiveness.