FYI: See awesome pictures for this (then June 2021, and updated this year) seemingly best method (none of the prior art seem better), even adding color to black-and-white faces. It’s not just denoising, also more specific, as for faces (intrigued to know what happens if not fed with faces):
See pictures with link above, but also different versions v1 (from paper), v1.2 and v1.3: https://github.com/TencentARC/GFPGAN/blob/master/Comparisons.md
“degradation model” (eq 10) is “first convolved with Gaussian blur kernel kσ followed by a downsampling operation with a scale factor r. After that, additive white Gaussian noise nδ is added to the image and finally it is compressed by JPEG with quality factor q”
2. Related Work
Image Restoration typically includes super-resolution [13, 48, 60, 49, 74, 68, 22, 50], denoising […]
Face Restoration. Based on general face hallucination [5, 30, 66, 70], two typical face-specific priors: geometry priors and reference priors, are incorporated to further improve the performance […]4.4. Discussion and Limitations
Training bias. Our method performs well on most dark-
skinned faces and various population groups (Fig. 8), as
our method uses both the pretrained GAN and input image
features for modulation.
DeblurGANv2 is sometimes better on some metric according to the paper above (and sometimes also other methods):
Deblurgan-v2: Deblurring (orders-of-magnitude) faster and better. In ICCV, 2019
DeblurGAN-v2 reaches 10-100 times faster than the nearest competitors, while maintaining close to state-of-the-art results, implying the option of real-time video deblurring.