Colorization is a computer-assisted process of adding color to a monochrome image or movie. The process typically involves segmenting images into regions and tracking these regions across image sequences. Neither of these tasks can be performed reliably in practice; consequently, colorization requires considerable user intervention and remains a tedious, time-consuming, and expensive task. In t...

Characterization of tissues like brain by using magnetic resonance (MR) images and colorization of the gray scale image has been reported in the literature, along with the advantages and drawbacks. Here, we present two independent methods; (i) a novel colorization method to underscore the variability in brain MR images, indicative of the underlying physical density of bio tissue, (ii) a segment...

We present a data-driven approach that colorizes 3D furniture models and indoor scenes by leveraging indoor images on the internet. Our approach is able to colorize the furniture automatically according to an example image. The core is to learn image-guided mesh segmentation to segment the model into different parts according to the imaged object. Given an indoor scene, the system supports colo...

We attempt to use DCGANs (deep convolutional generative adversarial nets) to tackle the automatic colorization of black and white photos to combat the tendency for vanilla neural nets to ”average out” the results. We construct a small feed-forward convolutional neural network as a baseline colorization system. We train the baseline model on the CIFAR-10 dataset with a per-pixel Euclidean loss f...

In the colorization process, artists fill image’s regions with colors following harmony rules. Some region colorizations are more significant than others. In this paper we introduce a model for computer assisted colorization of segmented images using Itten’s proportion contrast. Our model is able to process images composed of totally blank or partially filled regions. Though, each region must b...

We propose a hybrid recurrent Video Colorization with Hybrid Generative Adversarial Network (VCGAN), an improved approach to video colorization using end-to-end learning. The VCGAN addresses two prevalent issues in the domain: Temporal consistency and unification of network refinement into single architecture. To enhance quality spatiotemporal consistency, mainstream generator is assisted by ad...

Given a grayscale photograph, the colorization system estimates visually plausible colorful image. Conventional methods often use semantics to colorize images. However, in these methods, only classification semantic information is embedded, resulting confusion and color bleeding final colorized To address issues, we propose fully automatic Saliency Map-guided Colorization with Generative Advers...

Medical imaging is one of the most attractive topics of image processing and understanding research fields due to the similarity between the captured body organs colors. Most medical images come in grayscale with low contrast gray values; which makes it a challenge to discriminate between the region of interest (ROI) and the background (BG) parts. Pseudocoloring is one of the solutions to enhan...

In this paper we present how to exploit the textural information to improve scribble-based image colorization. Although many methods have been already proposed for coloring grayscale images based on a set of color scribbles inserted by a user, very few of them take into account textural properties. We demonstrate that the textural information can be extremely helpful for this purpose and it may...