Preface
Image coloring has always been an important topic in the field of digital image processing. Traditional methods of image coloring often require manual intervention, which is time-consuming and has limited effectiveness.
However, with the development of deep learning technology, automatic image coloring models have gradually become a research hotspot. Among them, the DDColor image coloring model has attracted much attention for its excellent performance and convenient use.
Project Introduction
DDColor is an image colorization model based on deep learning technology researched by Ali Damo Academy, which can automatically color black and white or grayscale images to make the images more vivid and realistic.
The model uses advanced neural network architecture and training techniques to recognize objects and scenes in images and add realistic colors to them.
Projects & Demos: https://modelscope.cn/models/damo/cv_ddcolor_image-colorization/summary
Thesis: https://arxiv.org/abs/2212.11613
GitHub：https://github.com/piddnad/DDColor
Colab Online Experience: https://github.com/camenduru/DDColor-colab
Dual decoder technology
The DDColor model includes an image encoder and two decoders, an image decoder and a color decoder, respectively. The image decoder completes the upsampling process of visual features, while the color decoder decodes the color query based on a Transformer.
Specifically, the color decoder uses multi-scale visual features to help the learning of color embeddings, and therefore learns strongly semantically correlated color embeddings.
Using dual decoder technology, DDColor is able to take into account both color distribution and pixel-level details, enabling highly realistic image coloring.
Not only can you color historical black and white photos, but you can also color landscapes from anime or games in a realistic style.
What does DDColor do with dual decoders?
DDColor uses two decoders to process the image: one to restore the structure of the image, and the other to determine the color of each part of the image.
The innovation of this technology is that it does not need to rely on manually set rules like the previous method, but is able to learn the content of the picture and decide on the appropriate color on its own.
In this way, DDColor can color images of complex scenes more accurately, reduce the problem of miscoloring, and make the final image look richer and more natural.
How DDColor works
Multi-scale processing of feature extraction, dual decoder structure, color application, and color richness optimization
Use and experience
1. Run the dependency installation
Method 1: If you have local or ECS computing resources, you can install them in the environment on the local or ECS to experience the algorithm model in a more flexible way.
Method 2: If you find the local installation complicated, you can also run the notebook provided by ModelScope (the official model platform of Alibaba Cloud) online.
The official image is pre-installed in the notebook, so there is no need for manual environment installation, which is more convenient and fast.
2. Image preparation
Prepare a black-and-white image or a color image (enter a color image, you can also recolor), the image can be local or on the network.
For example, let's choose a black and white photo:
3. Call pipeline for image coloring
import cv2
from modelscope.outputs import OutputKeys
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
img_colorization = pipeline(Tasks.image_colorization,
model='damo/cv_ddcolor_image-colorization')
img_path = 'https://modelscope.oss-cn-beijing.aliyuncs.com/test/images/audrey_hepburn.jpg'
result = img_colorization(img_path)
cv2.imwrite('result.png', result[OutputKeys.OUTPUT_IMG]) In this way, we get a color image (result.png) after coloring, and the effect is not bad!
More application scenarios
The DDColor image colorization model can be used in a wide range of fields such as:
- Film and television post-production: In film and television post-production, the DDColor model can be used to color old black and white movies or film and television dramas to make them more ornamental and commercially valuable.
- Artistic creation: Artists can use the DDColor model to add color to their black and white sketches to make their works more vivid.
- Historical photo restoration: DDColor models can also play an important role in the restoration and colorization of black and white historical photos.
summary
The DDColor image coloring model relies on advanced deep learning technology to provide users with a convenient and fast image coloring solution.
Its high-quality coloring effect and fast processing speed make it have a wide range of application prospects in many fields, bringing new possibilities to the field of image processing.