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Neumann Network With Recursive Kernels for Single Image Defocus Deblurring

CVPR 2023 · Image generation › Image restoration · approach: Image deblurring · 40 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use NRKNet rather than the strongest listed baselineDPDD-trained SIDD models · DPDD testPSNR: 25.468 -> 26.109Helped
Use NRKNet rather than the strongest listed baselineDPDD-trained SIDD models · RealDOFPSNR: 24.884 -> 25.148Helped
Use NRKNet rather than the strongest listed baselineDPDD-trained SIDD models · RTFPSNR: 24.972 -> 25.931Helped
Use NRKNet rather than the strongest listed baselineLFDOF-trained SIDD models · LFDOF testPSNR: 30.253 -> 30.481Helped
Use NRKNet rather than the strongest listed baselineLFDOF-trained SIDD models · RTFPSNR: 27.552 -> 28.047Helped
Learn atom kernels instead of using fixed Gaussian atoms (ablation)RKR in NRKNet · DPDDPSNR: 25.309 -> 26.109Helped
Use recursive rather than independent atoms (ablation)RKR in NRKNet · DPDDPSNR: 25.994 -> 26.109Helped
Use separable rather than non-separable atoms (ablation)RKR in NRKNet · DPDDPSNR: 26.071 -> 26.109Helped
Use cross-scale fusion rather than full Neumann expansion at every scale (ablation)NRKNet pipeline · DPDDPSNR: 26.077 -> 26.109Helped
Use three scales instead of two (ablation)NRKNet pipeline · DPDDPSNR: 25.894 -> 26.109Helped
Use three scales instead of four (ablation)NRKNet pipeline · DPDDPSNR: 26.020 -> 26.109Helped
Add ConvLSTM units across scales (ablation)NRKNet coefficient prediction · DPDDPSNR: 24.371 -> 26.109Helped

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