资源分类
》 资源 》论文》Stochastic Deconvolution

Stochastic Deconvolution

2019-11-28 | |  75 |   53 |   0

Abstract We present a novel stochastic framework for non-blind deconvolution based on point samples obtained from random walks. Unlike previous methods that must be tailored to specifific regularization strategies, the new Stochastic Deconvolution method allows arbitrary priors, including nonconvex and data-dependent regularizers, to be introduced and tested with little effort. Stochastic Deconvolution is straightforward to implement, produces state-of-the-art results and directly leads to a natural boundary condition for image boundaries and saturated pixels.

上一篇：Diffusion Processes for Retrieval Revisited

下一篇：Discriminative Brain Effective Connectivity Analysis for Alzheimer’s Disease: AKernel Learning Approach upon Sparse Gaussian Bayesian Network

用户评价
全部评价

热门资源

  • The Variational S...

    Unlike traditional images which do not offer in...

  • Learning to Predi...

    Much of model-based reinforcement learning invo...

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • A Mathematical Mo...

    Direct democracy, where each voter casts one vo...

  • Rating-Boosted La...

    The performance of a recommendation system reli...