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...

In this paper, we present an example-based colorization method to colorize a gray image. Besides the gray target image, the user only needs to provide a reference color image which is semantically similar to the gray image. We first segment both the target image and reference image and find correspondences at the segmentation level between these two images. The use of segmentation level can not...

In this paper, we present an interactive system for users to easily colorize the natural images of complex scenes. In our system, colorization procedure is explicitly separated into two stages: Color labeling and Color mapping. Pixels that should roughly share similar colors are grouped into coherent regions in the color labeling stage, and the color mapping stage is then introduced to further ...