Image Colorization With Deep Learning and Classification

We propose a fully automatic approach that produces vibrant and realistic colorizations. We embrace the underlying uncertainty of the problem.

This paper introduces a novel method for image colorization that utilizes a color transformer and generative adversarial networks (GANs)

We present MultiColor, a new learning-based approach to automatically colorize grayscale images that combines clues from multiple color spaces.

This repository contains a PyTorch implementation of the Colorful Image Colorization paper by Zhang et al., 2016. Some changes were made, namely:.

為了multimodel problem，作者為圖片像素預測一個顏色的機率分布，而不侷限特定顏色中。 Model. 將RGB轉為Lab，輸入 ...

We propose a fully automatic approach that produces vibrant and realistic colorizations. We embrace the underlying uncertainty of the problem.

We propose a fully automatic approach that produces vibrant and realistic colorizations. We embrace the underlying uncertainty of the problem. Rapeseed · Lorikeet · Cheeseburger · Meat Loaf

Abstract. Given a grayscale photograph as input, this paper attacks the problem of hallucinating a plausible color version of the photograph.

Colorization as pretext task in self-supervised learning, outperforms context prediction & context encoders.

This article presents a comprehensive survey of recent state-of-the-art deep learning-based image colorization techniques.