GAN-Based Image Deblurring Using DCT Loss With Customized Datasets
In this paper, we propose a high quality image deblurring method that uses discrete cosine transform (DCT) and requires less computational complexity. We train our model on a new dataset which is customized to include images with large motion blurs. Recently, Convolutional Neural Network (CNN) and G...
Main Authors: | Hiroki Tomosada, Takahiro Kudo, Takanori Fujisawa, Masaaki Ikehara |
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Format: | Article |
Language: | English |
Published: |
IEEE
2021-01-01
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Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9551883/ |
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