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Single Image Reflection Removal Through Cascaded Refinement

CVPR 2020 · Image generation › Image restoration · approach: Dehazing and deraining · 186 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use cascaded refinement with both transmission and reflection sub-networks rather than removing G_R (ablation)IBCLN · Nature, Zhang et al., and SIR²PSNR: 21.79 -> 23.57; 20.65 -> 21.86; 22.36 -> 24.20Helped
Use iterative refinement rather than one time step (ablation)IBCLN · Nature, Zhang et al., and SIR²PSNR: 21.82 -> 23.57; 20.49 -> 21.86; 23.09 -> 24.20Helped
Add adversarial loss L_adv (ablation)IBCLN · Nature and Zhang et al.PSNR: 23.24 -> 23.57; 21.74 -> 21.86Helped
Add adversarial loss L_adv (ablation)IBCLN · SIR²SSIM: 0.885 -> 0.884Hurt
Add residual reconstruction loss L_residual (ablation)IBCLN · Nature, Zhang et al., and SIR²PSNR: 22.54 -> 23.57; 20.98 -> 21.86; 23.74 -> 24.20Helped
Add multi-scale perceptual loss L_MP (ablation)IBCLN · Nature, Zhang et al., and SIR²PSNR: 23.14 -> 23.57; 21.47 -> 21.86; 22.96 -> 24.20Helped
Combine all loss terms rather than use only pixel loss (ablation)IBCLN · NaturePSNR: 21.98 -> 23.57Helped
Set the total cascade steps to 3 rather than fewer or more steps (ablation)IBCLN · SIR²PSNRHelped
Allow the network to learn the total number of steps automatically for each image (ablation)IBCLN · Reflection removal imagesperformanceHurt
Use IBCLN instead of ERRNet-FReflection removal · Average of five real-world test datasetsPSNR: 23.45 -> 24.08Helped
Use IBCLN instead of ERRNet-FReflection removal · Zhang et al.PSNR: 23.35 -> 21.86Hurt

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