Plug-and-play (PnP) priors constitute one of the most widely used frameworks for solving computational imaging problems through the integration of physical models and learned …
Learning to optimize (L2O) is an emerging approach that leverages machine learning to develop optimization methods, aiming at reducing the laborious iterations of hand …
Recent efforts on solving inverse problems in imaging via deep neural networks use architectures inspired by a fixed number of iterations of an optimization method. The number …
K Zhang, W Zuo, L Zhang - … of the IEEE/CVF conference on …, 2019 - openaccess.thecvf.com
While deep neural networks (DNN) based single image super-resolution (SISR) methods are rapidly gaining popularity, they are mainly designed for the widely-used bicubic …
In this work we introduce a novel stochastic algorithm dubbed SNIPS, which draws samples from the posterior distribution of any linear inverse problem, where the observation is …
S Hurault, A Leclaire… - … Conference on Machine …, 2022 - proceedings.mlr.press
Abstract Plug-and-Play (PnP) methods solve ill-posed inverse problems through iterative proximal algorithms by replacing a proximal operator by a denoising operation. When …
E Ryu, J Liu, S Wang, X Chen… - … on Machine Learning, 2019 - proceedings.mlr.press
Abstract Plug-and-play (PnP) is a non-convex framework that integrates modern denoising priors, such as BM3D or deep learning-based denoisers, into ADMM or other proximal …
Plug-and-Play methods constitute a class of iterative algorithms for imaging problems where regularization is performed by an off-the-shelf denoiser. Although Plug-and-Play methods …
Speckle is a granular deformity that frequently appears in images acquired through coherent imaging sensors such as synthetic aperture radar (SAR). The existence of such noise in the …