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About A Survey of Deep Learning Approaches To Image Restoration
This document provides a survey of deep learning approaches for image restoration tasks. It discusses how convolutional neural networks (CNNs) have achieved great success in image classification and are increasingly being used for image restoration problems like denoising, deblurring, dehazing, and super-resolution. The document reviews CNN architectures, loss functions, and evaluation metrics used in these image restoration tasks and summarizes the key principles and differences between discriminative and optimization-based deep learning methods. It also presents some new deep learning models developed by the authors for deblurring and super-resolution.
Detailed Information
| Author: | ['zwu1913'] |
|---|---|
| Publication Year: | 2023 |
| Pages: | 20 |
| Language: | English |
| Format: | |
| Price: | FREE |
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