请问有人能给我讲一下什么是概率成像吗？

Question

rt，导师让看一篇机器学习概率成像的文献，然后不知道概率成像的流程和原理，请问有人能给我讲一下吗

Answer 1

嗯...我觉得你导师让你看的是不是这一篇：

[PDF] Deep Probabilistic Imaging: Uncertainty Quantification and Multi-modal Solution Characterization for Computational Imaging | Semantic Scholar This paper proposes a variational deep probabilistic imaging approach to quantify reconstruction uncertainty and demonstrates this approach in the context of interferometric radio imaging, which is used for black hole imaging with the Event Horizon Telescope. Computational image reconstruction algorithms generally produce a single image without any measure of uncertainty or confidence. Regularized Maximum Likelihood (RML) and feed-forward deep learning approaches for inverse problems typically focus on recovering a point estimate. This is a serious limitation when working with underdetermined imaging systems, where it is conceivable that multiple image modes would be consistent with the measured data. Characterizing the space of probable images that explain the observational data is therefore crucial. In this paper, we propose a variational deep probabilistic imaging approach to quantify reconstruction uncertainty. Deep Probabilistic Imaging (DPI) employs an untrained deep generative model to estimate a posterior distribution of an unobserved image. This approach does not require any training data; instead, it optimizes the weights of a neural network to generate image samples that fit a particular measurement dataset. Once the network weights have been learned, the posterior distribution can be efficiently sampled. We demonstrate this approach in the context of interferometric radio imaging, which is used for black hole imaging with the Event Horizon Telescope. https://www.semanticscholar.org/paper/Deep-Probabilistic-Imaging%3A-Uncertainty-and-for-Sun-Bouman/0260c69e347ad2f47f65d43916425933bbde6b69#related-papers

https://arxiv.org/abs/2010.14462

GitHub - HeSunPU/DPI: Deep Probabilistic Imaging (DPI): Uncertainty Quantification and Multi-modal Solution Characterization for Computational Imaging Deep Probabilistic Imaging (DPI): Uncertainty Quantification and Multi-modal Solution Characterization for Computational Imaging - GitHub - HeSunPU/DPI: Deep Probabilistic Imaging (DPI): Uncertainty Quantification and Multi-modal Solution Characterization for Computational Imaging https://github.com/HeSunPU/DPI

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基本这个方向只有这一篇论文提到了，看来你只能慢慢啃了，
或者基于下面的相关论文进行下扩展阅读:
重点看第一篇，这个发表时间比其他文章都早，2017年的，可能更能看出来这个观点的原理和一些看法

[PDF] Deep-learning-based ghost imaging | Semantic Scholar Detailed comparisons between the image reconstructed using deep learning and compressive sensing shows that the proposed GIDL has a much better performance in extremely low sampling rate. In this manuscript, we propose a novel framework of computational ghost imaging, i.e., ghost imaging using deep learning (GIDL). With a set of images reconstructed using traditional GI and the corresponding ground-truth counterparts, a deep neural network was trained so that it can learn the sensing model and increase the quality image reconstruction. Moreover, detailed comparisons between the image reconstructed using deep learning and compressive sensing shows that the proposed GIDL has a much better performance in extremely low sampling rate. Numerical simulations and optical experiments were carried out for the demonstration of the proposed GIDL. https://www.semanticscholar.org/paper/Deep-learning-based-ghost-imaging-Lyu-Wang/b28f75371e04c39090c17c2caee2d98b758b2104

[PDF] Deep Gaussian Scale Mixture Prior for Spectral Compressive Imaging | Semantic Scholar This paper proposes a novel HSI reconstruction method based on the Maximum a Posterior (MAP) estimation framework using learned Gaussian Scale Mixture (GSM) prior through a deep convolutional neural network (DCNN). In coded aperture snapshot spectral imaging (CASSI) system, the real-world hyperspectral image (HSI) can be reconstructed from the captured compressive image in a snapshot. Model-based HSI reconstruction methods employed hand-crafted priors to solve the reconstruction problem, but most of which achieved limited success due to the poor representation capability of these hand-crafted priors. Deep learning based methods learning the mappings between the compressive images and the HSIs directly achieved much better results. Yet, it is nontrivial to design a powerful deep network heuristically for achieving satisfied results. In this paper, we propose a novel HSI reconstruction method based on the Maximum a Posterior (MAP) estimation framework using learned Gaussian Scale Mixture (GSM) prior. Different from existing GSM models using hand-crafted scale priors (e.g., the Jeffrey’s prior), we propose to learn the scale prior through a deep convolutional neural network (DCNN). Furthermore, we also propose to estimate the local means of the GSM models by the DCNN. All the parameters of the MAP estimation algorithm and the DCNN parameters are jointly optimized through end-to-end training. Extensive experimental results on both synthetic and real datasets demonstrate that the proposed method outperforms existing state-of-the-art methods. The code is available at https://see.xidian.edu.cn/faculty/wsdong/Projects/DGSM-SCI.htm. https://www.semanticscholar.org/paper/Deep-Gaussian-Scale-Mixture-Prior-for-Spectral-Huang-Dong/1cb314bbc7eec504d29632941331e3eaef9a82b0

[PDF] DeepGhost: real-time computational ghost imaging via deep learning | Semantic Scholar A fast image reconstruction framework for CGI, called “DeepGhost”, is proposed, using deep convolutional autoencoder network to achieve real-time imaging at very low sampling rates (10–20%). The potential of random pattern based computational ghost imaging (CGI) for real-time applications has been offset by its long image reconstruction time and inefficient reconstruction of complex diverse scenes. To overcome these problems, we propose a fast image reconstruction framework for CGI, called “DeepGhost”, using deep convolutional autoencoder network to achieve real-time imaging at very low sampling rates (10–20%). By transferring prior-knowledge from STL-10 dataset to physical-data driven network, the proposed framework can reconstruct complex unseen targets with high accuracy. The experimental results show that the proposed method outperforms existing deep learning and state-of-the-art compressed sensing methods used for ghost imaging under similar conditions. The proposed method employs deep architecture with fast computation, and tackles the shortcomings of existing schemes i.e., inappropriate architecture, training on limited data under controlled settings, and employing shallow network for fast computation. https://www.semanticscholar.org/paper/DeepGhost%3A-real-time-computational-ghost-imaging-Rizvi-Cao/26ef5486313ca170ddfff901cc95e6a4c0fadb07