Research map / Image generation / Image restoration
Robust Kernel Estimation With Outliers Handling for Image Deblurring
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the outlier-aware restoration model | Blind deblurring · Saturated image | PSNR: 27.08 -> 27.48 | Helped |
| Use the proposed kernel estimate with [4] instead of its standard kernel estimate | [4] non-blind restoration · Synthetic saturated image + impulse noise | PSNR: 18.42 -> 22.72 | Helped |
| Use the proposed kernel estimate with [34] instead of its standard kernel estimate | [34] non-blind restoration · Synthetic saturated image + impulse noise | PSNR: +3.57 | Helped |
| Add outlier mask M during kernel estimation (ablation) | Proposed method · Real image with saturated regions | visual quality | Helped |
| Select reliable intermediate edges and remove detected outliers before estimating the kernel | Blind deblurring methods · Images with saturated regions | kernel estimation quality | Helped |
| Use the proposed outlier-aware latent-image restoration model rather than TV-L1 | Final latent image restoration · Saturated image | PSNR: 19.45 -> 27.48 | Helped |
Closest papers by meaning
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