Image denoisers have been shown to be powerful priors for solving inverse problems in imaging. In this work, we introduce a generalization of these methodsthat allows any image restoration network to be used as an implicit prior. The pro-posed method uses priors specified by deep neural networks pre-trained as general restoration operators. The method provides a principled approach for adapting state-of-the-art restoration models for other inverse problems. Our theoreticalresult analyzes its convergence to a stationary point of a global functional associated with the restoration operator. Numerical results show that the method usinga super-resolution prior achieves state-of-the-art performance both quantitativelyand qualitatively. Overall, this work offers a step forward for solving inverse prob-lems by enabling the use of powerful pre-trained restoration models as priors.
The environment is
python=3.7.11, numpy=1.21.5, scipy==1.7.3, pytorch=1.10.2=py3.7_cuda11.3_cudnn8.2.0_0
The pre-trained models can be downloaded from Google drive.
Once downloaded, place them into ./model_zoo/swinir.
deblur:
python deblur_experments/DRP_deblur_k1.py
sisr:
python sisr_experments/DRP_sisr_2x_k1.py
denoise:
python denoising_experiments/DRP_denoising_2x.py