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Single Image Reflection Removal Exploiting Misaligned Training Data and Network Enhancements

CVPR 2019 · Image generation › Image restoration · approach: Reflection removal · 188 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add channel-wise context and multi-scale spatial context (ablation)BaseNet on Real20 · Real20PSNR: 21.51 -> 22.89Helped
add channel-wise context (ablation)BaseNet on Real20 · Real20PSNR: 21.51 -> 22.61Helped
add multi-scale spatial context (ablation)BaseNet on Real20 · Real20PSNR: 21.51 -> 21.75Helped
add channel-wise and multi-scale spatial context together (ablation)BaseNet on Synthetic · SyntheticPSNR: 25.71 -> 27.88Helped
add 50 aligned real images (ablation)ERRNet · Synthetic test imagesPSNR: 19.79 -> 22.00Helped
increase aligned real training images from 50 to 90 (ablation)ERRNet · Synthetic test imagesPSNR: 22.00 -> 22.89Helped
use pixel-wise loss for 40 unaligned images instead of aligned-invariant feature loss (ablation)ERRNet · Synthetic test imagesPSNR: 22.00 -> 21.85Hurt
add 40 unaligned images supervised with alignment-invariant loss (ablation)ERRNet · Synthetic test imagesPSNR: 22.00 -> 22.38Helped
add 40 unaligned images supervised with contextual loss (ablation)ERRNet · Synthetic test imagesPSNR: 22.00 -> 22.47Helped
combine invariant and contextual losses on 40 unaligned images (ablation)ERRNet · Synthetic test imagesPSNR: 22.00 -> 22.43Helped
finetune using collected unaligned pairs and alignment-invariant lossERRNet · Collected unaligned image pairshuman preference score: 0.51Helped
finetune using collected unaligned pairs and alignment-invariant lossBDN-F · Collected unaligned image pairshuman preference score: 0.62Helped

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