Overview of Colorization Transformer
Colorization Transformer is a complex probabilistic model used to add color to black and white images. A global receptive field with only two layers and a reduced complexity of $O(D\sqrt{D})$ instead of $O(D^2)$ are the main benefits of colorization transformer's axial self-attention blocks. To perform colorization on high-resolution grayscale images, the process is split into three simpler sequential tasks using a variation of Axial Transformer.
What is Colorization Transformer?
Colorization Transformer is a model used for adding color to black and white images. The model mainly comprises axial self-attention blocks, which makes it probabilistic. The axial self-attention blocks help to reduce the complexity of colorization transformer from $O(D^2)$ to $O(D\sqrt{D})$. Authors leverage the semi-parallel sampling mechanism of Axial Transformers to apply a conditional variant for coarse low-resolution colorization.
Advantages of Axial Self-Attention Blocks
Axial self-attention blocks have great advantages over traditional self-attention blocks. One primary advantage is their ability to capture a global receptive field with only two layers. Additionally, the colorization transformer's use of axial self-attention blocks reduces the complexity of computations required, making it more efficient.
Subtasks in Colorization Transformer
To colorize high-resolution grayscale images, the task is split into smaller sequential subtasks:
- Coarse Low Resolution Colorization: Colorization Transformer applies a conditional variant of Axial Transformer for coarse low-resolution colorization.
- Parallel Color: After the coarse low-resolution colorization, the model applies parallel color to the black and white image.
- Spatial Super-Resolution: The coarsely colorized image is super-resolved using fast parallel deterministic upsampling models.
How Does Colorization Transformer Work?
Colorization Transformer works by taking in a black and white image and colorizing it. First, it processes the image by applying a conditional variant of Axial Transformer for coarse low-resolution colorization. Next, regular parallel color is applied to the image. Finally, the coarsely colorized image undergoes spatial super-resolution with the help of fast parallel deterministic upsampling models. Through these three subtasks, the black and white image becomes nicely colored.
Applications of Colorization Transformer
Colorization transformer has become useful in many applications. One primary use is in colorizing black and white images. The technology has also been used in the entertainment industry to turn old black and white movies into colored ones. Furthermore, the technology can be used in various types of image recognition and image analysis systems.
In Conclusion
Colorization transformer is a complex probabilistic model used for adding color to black and white images. The model is composed of axial self-attention blocks, which gives it an advantage over other self-attention blocks. The process of colorizing high-resolution grayscale images is split into three simpler subtasks using a variation of Axial Transformer. The technology has many applications, including image recognition and analysis and colorization of black and white images.
