To remove these complicated motion blurs, conventional energy optimization based methods rely on simple assumptions such that blur kernel is partially uniform or locally linear.
Recent works have revealed that, with the aid of variable splitting techniques, denoiser prior can be plugged in as a modular part of model-based optimization methods to solve other inverse problems (e. g., deblurring).
#4 best model for Image Denoising on BSD68 sigma15
In this paper, we propose a principled formulation and framework by extending bicubic degradation based deep SISR with the help of plug-and-play framework to handle LR images with arbitrary blur kernels.
While machine learning approaches to image restoration offer great promise, current methods risk training models fixated on performing well only for image corruption of a particular level of difficulty---such as a certain level of noise or blur.
We propose a simple, interpretable framework for solving a wide range of image reconstruction problems such as denoising and deconvolution.
We show that the gradient of our prior corresponds to the mean-shift vector on the natural image distribution.
#31 best model for Image Super-Resolution on Set5 - 4x upscaling