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- W4292572219 abstract "For the problem of grayscale image colorization, many authors propose their methods to produce the most plausible, vivid images from a gray input. Almost all of them introduce a quite large neural network model with hundreds of megabytes of parameters. This paper proposes a relatively lightweight model to solve the problem which has equivalent performance to recent methods. Our model is based on famous U-net architecture which is frequently used for semantic segmentation problems. The model is trained to predict the chromatic ab channels given the lightness L channel in Lab color space to finally produce a colorful image. Our method applies self-supervised representation learning where input and labeled output are different channels of the same image. Experiments on commonly used PASCAL VOC 2012 and Places205 datasets show that our method has equivalent performance compared to other state-of-the-art algorithms while the model size is relatively smaller and consumes less computing resources." @default.
- W4292572219 created "2022-08-22" @default.
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- W4292572219 date "2022-01-01" @default.
- W4292572219 modified "2023-10-18" @default.
- W4292572219 title "A Lightweight Image Colorization Model Based on U-Net Architecture" @default.
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- W4292572219 doi "https://doi.org/10.1007/978-981-19-2894-9_7" @default.
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