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 ...

Colorization aims at recovering the original color of a monochrome image from only a few color pixels. A stateof-the-art approach is based on matrix completion, which assumes that the target color image is low-rank. However, this low-rank assumption is often invalid on natural images. In this paper, we propose a patch-based approach that divides the image into patches and then imposes a low-ran...

Remote sensing data have become increasingly vital in target detection, disaster monitoring, and military surveillance. Abundant pan-sharpening super-resolution (SR) methods based on deep learning been proposed achieved remarkable performance. However, requires paired panchromatic (PAN) multispectral (MS) images, SR cannot increase the spectral resolution of PAN. Thus, we introduce a computatio...

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...

Natural images in the colour space Y UV have been observed to have a non-Gaussian, heavy tailed distribution (called ’sparse’) when the filter