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Figure 1:Illustration of domain adaptation in PnP-ADMM. The mismatched denoiser is pre-trained on source distribution (BreCaHAD) and adapted to target distribution (MetFaces) using a few samples. Adapted prior is then plugged into PnP-ADMM algorithm to reconstruct a sample from MetFaces.
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Figure 3: Visual evaluation of PnP-ADMM on image super-resolution using denoisers trained on several datasets. Note how the disparities in the training distributions of denoisers directly influence the performance of PnP. The denoisers containing images most similar to MetFaces offer the best performance.
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Figure 4: Visual evaluation of several priors on the image super-resolution task reported in terms of PSNR (dB) and SSIM for an image from RxRx1. Note the influence of mismatched priors on the performance of PnP. .
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Figure 6: Visual comparison on super-resolution with target (MetFaces), mismatched (BreCaHAD), and adapted priors on two MetFaces test images. Note how the recovery performance increases by adaptation of mismatched priors to a larger set of images from the target distribution.
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Figure 7: Visual comparison of image super-resolution with target (RxRx1), mismatched (CelebA), and adapted priors on a test image from RxRx1. Note how the recovery performance increases by adaptation of mismatched priors to a larger set of images from the target distribution.
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