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Due to the development of deep learning-based denoisers, the plug-and-playstrategy has achieved great success in image restoration problems. However,existing plug-and-play image restoration methods are designed for non-blindGaussian denoising such as zhang et al (2022), the performance of which visiblydeteriorate for unknown noises. To push the limits of plug-and-play imagerestoration, we propose a novel framework with blind Gaussian prior, which candeal with more complicated image restoration problems in the real world. Morespecifically, we build up a new image restoration model by regarding the noiselevel as a variable, which is implemented by a two-stage blind Gaussiandenoiser consisting of a noise estimation subnetwork and a denoisingsubnetwork, where the noise estimation subnetwork provides the noise level tothe denoising subnetwork for blind noise removal. We also introduce thecurvature map into the encoder-decoder architecture and the supervisedattention module to achieve a highly flexible and effective convolutionalneural network. The experimental results on image denoising, deblurring andsingle-image super-resolution are provided to demonstrate the advantages of ourdeep curvature denoiser and the resulting plug-and-play blind image restorationmethod over the state-of-the-art model-based and learning-based methods. Ourmodel is shown to be able to recover the fine image details and tiny structureseven when the noise level is unknown for different image restoration tasks. Thesource codes are available at https://github.com/Duanlab123/CurvPnP.

CurvPnP: Plug-and-play Blind Image Restoration with Deep Curvature Denoiser