Colorization of grayscale images is a simple task for the human imagination.
Researchers from the Toyota Technological Institute at Chicago and University of Chicago developed a fully automatic image colorization system using deep learning and GPUs. Their paper mentions previous approaches required some level of user input.
Using a TITAN X GPU, they trained their deep neural network to predict hue and chroma distributions for each pixel given its hypercolumn descriptor. The predicted distributions then determine color assignment at test time.
With multiple applications that can benefit from automatic colorization (such as historical photographs and videos, artist assistance), the research strives to make colorization cost-effective and less-time consuming.
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Automatic Colorization of Grayscale Images
Apr 08, 2016
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AI-Generated Summary
- Researchers from the Toyota Technological Institute at Chicago and University of Chicago developed a fully automatic image colorization system using deep learning and GPUs.
- The system uses a deep neural network trained on a TITAN X GPU to predict hue and chroma distributions for each pixel, determining color assignment at test time.
- This technology has multiple applications, including colorizing historical photographs and videos, and assisting artists, making colorization more cost-effective and less time-consuming.
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