# Speeding up scaled gradient projection methods using deep neural networks for inverse problems in image processing

@article{Lee2019SpeedingUS, title={Speeding up scaled gradient projection methods using deep neural networks for inverse problems in image processing}, author={Byung Hyun Lee and Se Young Chun}, journal={ArXiv}, year={2019}, volume={abs/1902.02449} }

Conventional optimization based methods have utilized forward models with image priors to solve inverse problems in image processing. Recently, deep neural networks (DNN) have been investigated to significantly improve the image quality of the solution for inverse problems. Most DNN based inverse problems have focused on using data-driven image priors with massive amount of data. However, these methods often do not inherit nice properties of conventional approaches using theoretically well… Expand

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Multiple Norms and Boundary Constraint Enforced Image Deblurring via Efficient MCMC Algorithm

- Computer Science
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This letter proposes a new image deblurring algorithm, called MNBC-Gibbs (multiple norms and boundary constraint enforced Gibbs sampling), where the quadratic and sparseness-inducing norms are combined to construct regularization term, and the objective function is gradually minimized without requirement of regularization parameter choice. Expand

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