Variational Models for Image Colorization via Chromaticity and Brightness Decomposition

Implicit Procedures for PDE-based Color Image Denoising via Brightness-Chromaticity Decomposition∗

Among various studies for color image denoising, the methods based on the chromaticity-brightness decomposition are known to result in relatively better restored images. This article begins with a generalization of the chromaticitybrightness model in the angle domain, hybridizing the total-variation minimization and the mean-curvature flow. For a reliable preservation of the edges, we suggest a...

A Fast Algorithm for Vectorial Tv-based Image Restoration

In this paper, we firstly extend a simple algorithm proposed by Jia et al. [16] to color/vectorial images, and then apply it to some variational models for image restoration including color image denoising with the RGB and chromaticity-brightness(CB) color representation, chromaticity-brightness(CB) based colorization and inpainting etc. The variational models are all total variation(TV)-based ...

Automatic Image Colorization Via Multimodal Predictions

We aim to color greyscale images automatically, without any manual intervention. The color proposition could then be interactively corrected by user-provided color landmarks if necessary. Automatic colorization is nontrivial since there is usually no one-to-one correspondence between color and local texture. The contribution of our framework is that we deal directly with multimodality and estim...

Color image enhancement via chromaticity diffusion

A novel approach for color image denoising is proposed in this paper. The algorithm is based on separating the color data into chromaticity and brightness, and then processing each one of these components with partial differential equations or diffusion flows. In the proposed algorithm, each color pixel is considered as an n-dimensional vector. The vectors' direction, a unit vector, gives the c...

Variational domain decomposition for parallel image processing