An Interactive Tool for Gamut Masking

Artists often want to change the colors of an image to achieve a particular aesthetic goal. For example, they might limit colors to a warm or cool color scheme to create an image with a certain mood or feeling. Gamut masking is a technique that artists use to limit the set of colors they can paint with. They draw a mask over a color wheel and only use the hues within the mask. However, creating the color palette from the mask and applying the colors to the image requires skill. We propose an interactive tool for gamut masking that allows amateur artists to create an image with a desired mood or feeling. Our system extracts a 3D color gamut from the 2D user-drawn mask and maps the image to this gamut. The user can draw a different gamut mask or locally refine the image colors. Our voxel grid gamut representation allows us to represent gamuts of any shape, and our cluster-based image representation allows the user to change colors locally

Example-based image color and tone style enhancement

Color and tone adjustments are among the most frequent image enhancement operations. We define a color and tone style as a set of explicit or implicit rules governing color and tone adjustments. Our goal in this paper is to learn implicit color and tone adjustment rules from examples. That is, given a set of examples, each of which is a pair of corresponding images before and after adjustments, we would like to discover the underlying mathematical relationships optimally connecting the color and tone of corresponding pixels in all image pairs. We formally define tone and color adjustment rules as mappings, and propose to approximate complicated spatially varying nonlinear mappings in a piecewise manner. The reason behind this is that a very complicated mapping can still be locally approximated with a low-order polynomial model. Parameters within such low-order models are trained using data extracted from example image pairs. We successfully apply our framework in two scenarios, low-quality photo enhancement by transferring the style of a high-end camera, and photo enhancement using styles learned from photographers and designers. © 2011 ACM.postprin

Sparse graph regularized mesh color edit propagation

Mesh color edit propagation aims to propagate the color from a few color strokes to the whole mesh, which is useful for mesh colorization, color enhancement and color editing, etc. Compared with image edit propagation, luminance information is not available for 3D mesh data, so the color edit propagation is more difficult on 3D meshes than images, with far less research carried out. This paper proposes a novel solution based on sparse graph regularization. Firstly, a few color strokes are interactively drawn by the user, and then the color will be propagated to the whole mesh by minimizing a sparse graph regularized nonlinear energy function. The proposed method effectively measures geometric similarity over shapes by using a set of complementary multiscale feature descriptors, and effectively controls color bleeding via a sparse ℓ 1 optimization rather than quadratic minimization used in existing work. The proposed framework can be applied for the task of interactive mesh colorization, mesh color enhancement and mesh color editing. Extensive qualitative and quantitative experiments show that the proposed method outperforms the state-of-the-art methods