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About Image Colorization Final Report
This document provides an introduction and overview of image colorization. It discusses how colorization adds color components to grayscale images to provide more semantic insights. Colorization is challenging due to limited datasets and computational resources. Deep learning models like CNNs and the U-Net architecture are commonly used for colorization tasks. The U-Net consists of an encoding path to extract features and a decoding path to reconstruct the image, and it is well-suited for colorization due to its similarities to segmentation. The motivation for colorization is to enhance grayscale images, colorize historical photos, and aid in digital art creation. The aim is to develop models that can accurately and realistically colorize images.
Detailed Information
| Author: | ['city'] |
|---|---|
| Publication Year: | 2024 |
| Pages: | 52 |
| Language: | English |
| Format: | |
| Price: | FREE |
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