Proceedings ArticleDOI
ReconNet: Non-Iterative Reconstruction of Images from Compressively Sensed Measurements
Kuldeep Kulkarni,Suhas Lohit,Pavan Turaga,Ronan Kerviche,Amit Ashok +4 more
- pp 449-458
TLDR
A novel convolutional neural network architecture which takes in CS measurements of an image as input and outputs an intermediate reconstruction which is fed into an off-the-shelf denoiser to obtain the final reconstructed image, ReconNet.Abstract:
The goal of this paper is to present a non-iterative and more importantly an extremely fast algorithm to reconstruct images from compressively sensed (CS) random measurements. To this end, we propose a novel convolutional neural network (CNN) architecture which takes in CS measurements of an image as input and outputs an intermediate reconstruction. We call this network, ReconNet. The intermediate reconstruction is fed into an off-the-shelf denoiser to obtain the final reconstructed image. On a standard dataset of images we show significant improvements in reconstruction results (both in terms of PSNR and time complexity) over state-of-the-art iterative CS reconstruction algorithms at various measurement rates. Further, through qualitative experiments on real data collected using our block single pixel camera (SPC), we show that our network is highly robust to sensor noise and can recover visually better quality images than competitive algorithms at extremely low sensing rates of 0.1 and 0.04. To demonstrate that our algorithm can recover semantically informative images even at a low measurement rate of 0.01, we present a very robust proof of concept real-time visual tracking application.read more
Citations
More filters
Journal ArticleDOI
Deep Convolutional Neural Network for Inverse Problems in Imaging
TL;DR: In this paper, the authors proposed a deep convolutional neural network (CNN)-based algorithm for solving ill-posed inverse problems, which combines multiresolution decomposition and residual learning in order to learn to remove these artifacts while preserving image structure.
Journal ArticleDOI
A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction
TL;DR: A framework for reconstructing dynamic sequences of 2-D cardiac magnetic resonance images from undersampled data using a deep cascade of convolutional neural networks (CNNs) to accelerate the data acquisition process is proposed and it is demonstrated that CNNs can learn spatio-temporal correlations efficiently by combining convolution and data sharing approaches.
Proceedings ArticleDOI
ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing
Jian Zhang,Bernard Ghanem +1 more
TL;DR: This paper proposes a novel structured deep network, dubbed ISTA-Net, which is inspired by the Iterative Shrinkage-Thresholding Algorithm (ISTA) for optimizing a general $$ norm CS reconstruction model and develops an effective strategy to solve the proximal mapping associated with the sparsity-inducing regularizer using nonlinear transforms.
Journal ArticleDOI
Using Deep Neural Networks for Inverse Problems in Imaging: Beyond Analytical Methods
TL;DR: The popular neural network architectures used for imaging tasks are reviewed, offering some insight as to how these deep-learning tools can solve the inverse problem.
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.
References
More filters
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.
Proceedings ArticleDOI
Fully convolutional networks for semantic segmentation
TL;DR: The key insight is to build “fully convolutional” networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning.
Proceedings ArticleDOI
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
TL;DR: RCNN as discussed by the authors combines CNNs with bottom-up region proposals to localize and segment objects, and when labeled training data is scarce, supervised pre-training for an auxiliary task, followed by domain-specific fine-tuning, yields a significant performance boost.
Book
Compressed sensing
TL;DR: It is possible to design n=O(Nlog(m)) nonadaptive measurements allowing reconstruction with accuracy comparable to that attainable with direct knowledge of the N most important coefficients, and a good approximation to those N important coefficients is extracted from the n measurements by solving a linear program-Basis Pursuit in signal processing.
Journal ArticleDOI
Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
TL;DR: In this paper, the authors considered the model problem of reconstructing an object from incomplete frequency samples and showed that with probability at least 1-O(N/sup -M/), f can be reconstructed exactly as the solution to the lscr/sub 1/ minimization problem.