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About Deblurgan: Blind Motion Deblurring Using Conditional Adversarial Networks
This document summarizes the DeblurGAN method for blind motion deblurring using conditional adversarial networks. DeblurGAN achieves state-of-the-art performance in deblurring quality and speed. It uses a conditional GAN with a Wasserstein GAN critic and perceptual loss to generate perceptually realistic sharp images from blurred inputs. DeblurGAN is also evaluated based on its ability to improve object detection on blurred images. The model and code are available online for others to use.
Detailed Information
| Author: | ['Akshay Harlalka'] |
|---|---|
| Publication Year: | 2019 |
| Pages: | 10 |
| Language: | English |
| Format: | |
| Price: | FREE |
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