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Unpaired Deep Image Deraining Using Dual Contrastive Learning

CVPR 2022 · Image generation › Image restoration · approach: Dehazing and deraining · 200 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use DCD-GAN instead of the strongest listed baselineunpaired image deraining · Test100PSNR: 24.37 -> 25.61Helped
Use DCD-GAN instead of the strongest listed baselineunpaired image deraining · Test1200SSIM: 0.934 -> 0.937Helped
Use DCD-GAN instead of the strongest listed baselineunpaired image deraining · Test1400PSNR: 32.03 -> 31.65Hurt
Use DCD-GAN instead of the strongest listed baselineunpaired image deraining · Test1000SSIM: 0.950 -> 0.943Hurt
Use DCD-GAN instead of the strongest listed baselinereal-world image deraining · Real-world rainy imagesNIQE: 4.263 -> 4.032Helped
Add adversarial loss (ablation)DCD-GAN · Test100PSNR: 24.03 -> 25.61Helped
Add color cycle-consistency loss (ablation)DCD-GAN · Test100PSNR: 25.08 -> 25.61Helped
Use color cycle-consistency loss instead of standard cycle-consistency loss (ablation)DCD-GAN · Test100PSNR: 25.32 -> 25.61Helped
Add contrastive loss (ablation)DCD-GAN · Test100PSNR: 22.84 -> 25.61Helped
Add frequency loss (ablation)DCD-GAN · Test100PSNR: 25.40 -> 25.61Helped
Use internal and external negatives instead of internal negatives only (ablation)DCD-GAN · Test100PSNR: 25.47 -> 25.61Helped
Use internal and external negatives instead of internal negatives only (ablation)DCD-GAN · Test100SSIM: 0.815 -> 0.813Hurt

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