Deep Fully-Connected Networks for Video Compressive Sensing
TLDR
In this article, a deep learning framework for video compressive sensing is presented, which enables recovery of video frames in a few seconds at significantly improved reconstruction quality compared to previous approaches.About:
This article is published in Digital Signal Processing.The article was published on 2018-01-01 and is currently open access. It has received 147 citations till now. The article focuses on the topics: Deep learning.read more
Citations
More filters
Journal ArticleDOI
ADMM-CSNet: A Deep Learning Approach for Image Compressive Sensing
TL;DR: Two versions of a novel deep learning architecture are proposed, dubbed as ADMM-CSNet, by combining the traditional model-based CS method and data-driven deep learning method for image reconstruction from sparsely sampled measurements, which achieved favorable reconstruction accuracy in fast computational speed compared with the traditional and the other deep learning methods.
Journal ArticleDOI
A Survey of Deep Learning: Platforms, Applications and Emerging Research Trends
William G. Hatcher,Wei Yu +1 more
TL;DR: A thorough investigation of deep learning in its applications and mechanisms is sought, as a categorical collection of state of the art in deep learning research, to provide a broad reference for those seeking a primer on deep learning and its various implementations, platforms, algorithms, and uses in a variety of smart-world systems.
Journal ArticleDOI
DR2-Net: Deep Residual Reconstruction Network for image compressive sensing
TL;DR: A novel Deep Residual Reconstruction Network (DR2-Net) to reconstruct the image from its Compressively Sensed measurement by outperforms traditional iterative methods and recent deep learning-based methods by large margins at measurement rates 0.01, 0.1, and 0.25.
Proceedings ArticleDOI
lambda-Net: Reconstruct Hyperspectral Images From a Snapshot Measurement
TL;DR: The λ-net, which reconstructs hyperspectral images from a single shot measurement, can finish the reconstruction task within sub-seconds instead of hours taken by the most recently proposed DeSCI algorithm, thus speeding up the reconstruction >1000 times.
Journal ArticleDOI
Snapshot Compressive Imaging: Principle, Implementation, Theory, Algorithms and Applications.
TL;DR: In this article, a review of recent advances in Snapshot compressive imaging hardware, theory and algorithms, including both optimization-based and deep learning-based algorithms, is presented.
References
More filters
Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
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.
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
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.
Journal ArticleDOI
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.
Journal ArticleDOI
Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
Journal ArticleDOI
Image quality assessment: from error visibility to structural similarity
TL;DR: In this article, a structural similarity index is proposed for image quality assessment based on the degradation of structural information, which can be applied to both subjective ratings and objective methods on a database of images compressed with JPEG and JPEG2000.