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SL-CycleGAN: Blind Motion Deblurring in Cycles Using Sparse Learning
- Source :
- 2022 7th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA).
- Publication Year :
- 2022
- Publisher :
- IEEE, 2022.
-
Abstract
- In this paper, we introduce an end-to-end generative adversarial network (GAN) based on sparse learning for single image blind motion deblurring, which we called SL-CycleGAN. For the first time in blind motion deblurring, we propose a sparse ResNet-block as a combination of sparse convolution layers and a trainable spatial pooler k-winner based on HTM (Hierarchical Temporal Memory) to replace non-linearity such as ReLU in the ResNet-block of SL-CycleGAN generators. Furthermore, unlike many state-of-the-art GAN-based motion deblurring methods that treat motion deblurring as a linear end-to-end process, we take our inspiration from the domain-to-domain translation ability of CycleGAN, and we show that image deblurring can be cycle-consistent while achieving the best qualitative results. Finally, we perform extensive experiments on popular image benchmarks both qualitatively and quantitatively and achieve the record-breaking PSNR of 38.087 dB on GoPro dataset, which is 5.377 dB better than the most recent deblurring method.<br />Comment: 12 pages
- Subjects :
- FOS: Computer and information sciences
Computer Vision and Pattern Recognition (cs.CV)
Image and Video Processing (eess.IV)
Computer Science - Computer Vision and Pattern Recognition
FOS: Electrical engineering, electronic engineering, information engineering
Electrical Engineering and Systems Science - Image and Video Processing
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2022 7th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA)
- Accession number :
- edsair.doi.dedup.....b585b6483c9dafc11bc448c5a11e87e2