9,765 research outputs found
Single-Pixel Image Reconstruction Based on Block Compressive Sensing and Deep Learning
Single-pixel imaging (SPI) is a novel imaging technique whose working
principle is based on the compressive sensing (CS) theory. In SPI, data is
obtained through a series of compressive measurements and the corresponding
image is reconstructed. Typically, the reconstruction algorithm such as basis
pursuit relies on the sparsity assumption in images. However, recent advances
in deep learning have found its uses in reconstructing CS images. Despite
showing a promising result in simulations, it is often unclear how such an
algorithm can be implemented in an actual SPI setup. In this paper, we
demonstrate the use of deep learning on the reconstruction of SPI images in
conjunction with block compressive sensing (BCS). We also proposed a novel
reconstruction model based on convolutional neural networks that outperforms
other competitive CS reconstruction algorithms. Besides, by incorporating BCS
in our deep learning model, we were able to reconstruct images of any size
above a certain smallest image size. In addition, we show that our model is
capable of reconstructing images obtained from an SPI setup while being priorly
trained on natural images, which can be vastly different from the SPI images.
This opens up opportunity for the feasibility of pretrained deep learning
models for CS reconstructions of images from various domain areas
Spatially Directional Predictive Coding for Block-based Compressive Sensing of Natural Images
A novel coding strategy for block-based compressive sens-ing named spatially
directional predictive coding (SDPC) is proposed, which efficiently utilizes
the intrinsic spatial cor-relation of natural images. At the encoder, for each
block of compressive sensing (CS) measurements, the optimal pre-diction is
selected from a set of prediction candidates that are generated by four
designed directional predictive modes. Then, the resulting residual is
processed by scalar quantiza-tion (SQ). At the decoder, the same prediction is
added onto the de-quantized residuals to produce the quantized CS measurements,
which is exploited for CS reconstruction. Experimental results substantiate
significant improvements achieved by SDPC-plus-SQ in rate distortion
performance as compared with SQ alone and DPCM-plus-SQ.Comment: 5 pages, 3 tables, 3 figures, published at IEEE International
Conference on Image Processing (ICIP) 2013 Code Avaiable:
http://idm.pku.edu.cn/staff/zhangjian/SDPC
Compressive Imaging Using RIP-Compliant CMOS Imager Architecture and Landweber Reconstruction
In this paper, we present a new image sensor architecture for fast and accurate compressive sensing (CS) of natural images. Measurement matrices usually employed in CS CMOS image sensors are recursive pseudo-random binary matrices. We have proved that the restricted isometry property of these matrices is limited by a low sparsity constant. The quality of these matrices is also affected by the non-idealities of pseudo-random number generators (PRNG). To overcome these limitations, we propose a hardware-friendly pseudo-random ternary measurement matrix generated on-chip by means of class III elementary cellular automata (ECA). These ECA present a chaotic behavior that emulates random CS measurement matrices better than other PRNG. We have combined this new architecture with a block-based CS smoothed-projected Landweber reconstruction algorithm. By means of single value decomposition, we have adapted this algorithm to perform fast and precise reconstruction while operating with binary and ternary matrices. Simulations are provided to qualify the approach.Ministerio de Economía y Competitividad TEC2015-66878-C3-1-RJunta de Andalucía TIC 2338-2013Office of Naval Research (USA) N000141410355European Union H2020 76586
Video Compressive Sensing for Dynamic MRI
We present a video compressive sensing framework, termed kt-CSLDS, to
accelerate the image acquisition process of dynamic magnetic resonance imaging
(MRI). We are inspired by a state-of-the-art model for video compressive
sensing that utilizes a linear dynamical system (LDS) to model the motion
manifold. Given compressive measurements, the state sequence of an LDS can be
first estimated using system identification techniques. We then reconstruct the
observation matrix using a joint structured sparsity assumption. In particular,
we minimize an objective function with a mixture of wavelet sparsity and joint
sparsity within the observation matrix. We derive an efficient convex
optimization algorithm through alternating direction method of multipliers
(ADMM), and provide a theoretical guarantee for global convergence. We
demonstrate the performance of our approach for video compressive sensing, in
terms of reconstruction accuracy. We also investigate the impact of various
sampling strategies. We apply this framework to accelerate the acquisition
process of dynamic MRI and show it achieves the best reconstruction accuracy
with the least computational time compared with existing algorithms in the
literature.Comment: 30 pages, 9 figure
Compressive image sensor architecture with on-chip measurement matrix generation
A CMOS image sensor architecture that uses a cellular automaton for the pseudo-random compressive sampling matrix generation is presented. The image sensor employs in-pixel pulse-frequency modulation and column wise pulse counters to produce compressed samples. A common problem of compressive sampling applied to image sensors is that the size of a full-frame compressive strategy is too large to be stored in an on-chip memory. Since this matrix has to be transmitted to or from the reconstruction system its size would also prevent practical applications. A full-frame compressive strategy generated using a 1-D cellular automaton showing a class III behavior neither needs a storage memory nor needs to be continuously transmitted. In-pixel pulse frequency modulation and up-down counters allow the generation of differential compressed samples directly in the digital domain where it is easier to improve the required dynamic range. These solutions combined together improve the accuracy of the compressed samples thus improving the performance of any generic reconstruction algorithm.Ministerio de Economía y Competitividad TEC2015-66878-C3-1-RJunta de Andalucía TIC 2338-2013Office of Naval Research (USA) N00014141035
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