Open AccessBook
Compressive Imaging: Structure, Sampling, Learning
Ben Adcock,Anders C. Hansen +1 more
- 16 Sep 2021
43
TL;DR: An in-depth treatment of compressive imaging, with an eye to the next decade of imaging research, and using both empirical and mathematical insights, examines the potential benefits and the pitfalls of these latest approaches.
read more
Abstract: Accurate, robust and fast image reconstruction is a critical task in many scientific, industrial and medical applications. Over the last decade, image reconstruction has been revolutionized by the rise of compressive imaging. It has fundamentally changed the way modern image reconstruction is performed. This in-depth treatment of the subject commences with a practical introduction to compressive imaging, supplemented with examples and downloadable code, intended for readers without extensive background in the subject. Next, it introduces core topics in compressive imaging – including compressed sensing, wavelets and optimization – in a concise yet rigorous way, before providing a detailed treatment of the mathematics of compressive imaging. The final part is devoted to recent trends in compressive imaging: deep learning and neural networks. With an eye to the next decade of imaging research, and using both empirical and mathematical insights, it examines the potential benefits and the pitfalls of these latest approaches.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Foundations Of Image Science
Melanie Grunwald
- 01 Jan 2016
TL;DR: The foundations of image science is universally compatible with any devices to read, and is available in the book collection an online access to it is set as public so you can download it instantly.
320
The difficulty of computing stable and accurate neural networks: On the barriers of deep learning and Smale’s 18th problem
TL;DR: This work begins a classification theory on which NNs can be trained and introduces NNs that—under suitable conditions—are robust to perturbations and exponentially accurate in the number of hidden layers.
118
Journal Article
Towards optimal sampling for learning sparse approximation in high dimensions
TL;DR: This chapter discusses recent work on learning sparse approximations to high-dimensional functions on data, where the target functions may be scalar-, vectoror even Hilbert space-valued, and describes a general construction of sampling measures that improves over standard Monte Carlo sampling.
10
On efficient algorithms for computing near-best polynomial approximations to high-dimensional, Hilbert-valued functions from limited samples
TL;DR: A novel restarted version of the primal-dual iteration for solving weighted (cid:96) 1 -minimization problems in Hilbert spaces and establishes error bounds for these algorithms which provably achieve the same algebraic or exponential rates as those of the best s -term approximation.
References
Deep Residual Learning for Image Recognition
Kaiming He,Xiangyu Zhang,Shaoqing Ren,Jian Sun +3 more
- 27 Jun 2016
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
U-Net: Convolutional Networks for Biomedical Image Segmentation
Olaf Ronneberger,Philipp Fischer,Thomas Brox +2 more
- 05 Oct 2015
TL;DR: Neber et al. as discussed by the authors proposed a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently, which can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
•Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
Alex Krizhevsky,Ilya Sutskever,Geoffrey E. Hinton +2 more
- 03 Dec 2012
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
67K
Generative Adversarial Nets
Ian Goodfellow,Jean Pouget-Abadie,Mehdi Mirza,Bing Xu,David Warde-Farley,Sherjil Ozair,Aaron Courville,Yoshua Bengio +7 more
- 08 Dec 2014
TL;DR: A new framework for estimating generative models via an adversarial process, in which two models are simultaneously train: a generative model G that captures the data distribution and a discriminative model D that estimates the probability that a sample came from the training data rather than G.
Related Papers (5)
George Barbastathis,Aydogan Ozcan,Guohai Situ +2 more
- 20 Aug 2019
Kerstin Hammernik,Kerstin Hammernik,Florian Knoll +2 more
- 01 Jan 2020