Exemplar-based colorization approaches rely on reference image to provide plausible colors for target gray-scale image. The key and difficulty of exemplar-based is establish an accurate correspondence between these two images. Previous have attempted construct such a but are faced with obstacles. First, using luminance channel the calculation inaccurate. Second, dense they built introduces wron...

A novel semiautomatic colorization method is proposed based on quaternionic phase reconstruction. In this method, each color pixel is represented as a quaternion, whose polar magnitude and polar phase are recovered from the intensity of original grayscale image and color scribbles of user’s manual input, respectively. To conduct structure-preserving colorization, color diffusion is restrained a...

For the culmination of the course CMPS 242, Machine Learning, the authors 1 present a method for image colorization using convolutional neural networks. 2 Colorization, taking a black and white image and turning into a color (RGB) image, 3 is inherently an underdetermined problem. Because of this we aim to generate 4 plausible colorizations using the technology of convolutional neural networks. 5

We propose a novel video colorization method based on sparse optical flow and edge-oriented color propagation. Colorization is a process of adding color to monochrome images or videos. In our video colorization method, it is assumed that key frames are appropriately selected out of a grayscale video stream and are properly colorized in advance. Once key frames are colorized, our method colorize...

Over the last decade, the process of automatic colorization had been studied thoroughly due to its vast application such as colorization of grayscale images and restoration of aged and/or degraded images. This problem is highly ill-posed due to the extremely large degrees of freedom during the assignment of color information. Many of the recent developments in automatic colorization involved im...

The detailed performance comparison of four orthogonal transforms for grayscale image colorization using Thepade’s Transform Error Vector Rotation(TTEVR) algorithm is done here with five different color pallet sizes. The Thepade’s transform error vector rotation algorithms use binary numbers represented by four error vector sequences (Haar, Cosine, Slant & Hartley). The proposed technique uses ...

This project seeks to reproduce the work of [Levin] on colorization of a black and white image. The approach used takes two black and white images, which are identical except for color markings on one of them. The colored result is based on an optimization problem that has two sets of constraints: First, the color of pixels should be close to their marked color, if applicable. Second, the color...