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The top half compares FlatNet-sep with Tikh+U-Net for separable lensless model while the bottom half compares FlatNet-gen with the corresponding Tikh+U-Net. FlatNet outperforms Tikh+U-Net for both separable and non-separable models because it learns an end-to-end mapping.
| Methods | PSNR (in dB) | LPIPS |
|---|---|---|
| Separable Model | ||
|
| ||
| Tikh+U-Net | 18.90 | 0.322 |
| FlatNet | 19.62 | 0.256 |
|
| ||
| Non-separable Model | ||
|
| ||
| Tikh+U-Net | 20.60 | 0.298 |
| FlatNet | 20.94 | 0.296 |
