Multitrack Compressed Sensing for Faster Hyperspectral Imaging

<div>Hyperspectral imaging (HSI) provides much more information than regular optical microscopy and spectroscopy, making it valuable in areas such as biomedicine, materials inspection and food safety. However HSI is challenging because of the large amount of data that has to be acquired, and large measurement times. Compressed sensing (CS) approaches to hyperspectral imaging have been developed to address this, albeit subject to tradeoffs between image reconstruction accuracy, time and generalizability to different types objects or scenes.</div><div>Here, we develop improved compressed sensing approaches for HSI, based on parallelized multitrack acquisition of multiple spectra per shot. This is an augmentation of single-pixel-camera-style acquisition for HSI, where a single spectrum is measured per shot. The multitrack architecture can be paired up with either of the two compatible CS algorithms developed here: (1) a sparse recovery algorithm based on block compressed sensing, and (2) an adaptive CS algorithm based on sampling in the wavelet domain. As a result, the measurement speed can be drastically increased, while maintaining reconstruction speed as well as accuracy.</div><div>The methods are validated computationally, via noiseless as well as noisy simulated measurements. Multitrack adaptive CS has a ~10 times smaller measurement plus reconstruction time as compared to full sampling HSI, without compromise to reconstruction accuracies across the different sample images tested. Multitrack non-adaptive CS (sparse recovery) suffers a large reconstruction time, but is the most robust to Poisson noise.</div><div><i><b>Note: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.</b></i><br></div>

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Multitrack Compressed Sensing for Faster Hyperspectral Imaging

10.36227/techrxiv.13667882 ◽

2021 ◽

Author(s):

Sharvaj Kubal ◽

Elizabeth Lee ◽

Chor Yong Tay ◽

Derrick Yong

Keyword(s):

Compressed Sensing ◽

Hyperspectral Imaging ◽

Sparse Recovery ◽

Single Spectrum ◽

Recovery Algorithm ◽

Different Types ◽

Block Compressed Sensing ◽

Large Measurement ◽

Single Pixel ◽

Reconstruction Time

<div>Hyperspectral imaging (HSI) provides much more information than regular optical microscopy and spectroscopy, making it valuable in areas such as biomedicine, materials inspection and food safety. However HSI is challenging because of the large amount of data that has to be acquired, and large measurement times. Compressed sensing (CS) approaches to hyperspectral imaging have been developed to address this, albeit subject to tradeoffs between image reconstruction accuracy, time and generalizability to different types objects or scenes.</div><div>Here, we develop improved compressed sensing approaches for HSI, based on parallelized multitrack acquisition of multiple spectra per shot. This is an augmentation of single-pixel-camera-style acquisition for HSI, where a single spectrum is measured per shot. The multitrack architecture can be paired up with either of the two compatible CS algorithms developed here: (1) a sparse recovery algorithm based on block compressed sensing, and (2) an adaptive CS algorithm based on sampling in the wavelet domain. As a result, the measurement speed can be drastically increased, while maintaining reconstruction speed as well as accuracy.</div><div>The methods are validated computationally, via noiseless as well as noisy simulated measurements. Multitrack adaptive CS has a ~10 times smaller measurement plus reconstruction time as compared to full sampling HSI, without compromise to reconstruction accuracies across the different sample images tested. Multitrack non-adaptive CS (sparse recovery) suffers a large reconstruction time, but is the most robust to Poisson noise.</div><div><i><b>Note: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.</b></i><br></div>

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Sparse Recovery Algorithm for Compressed Sensing Using Smoothed l0 Norm and Randomized Coordinate Descent

Mathematics ◽

10.3390/math7090834 ◽

2019 ◽

Vol 7 (9) ◽

pp. 834

Author(s):

Jin ◽

Yang ◽

Li ◽

Liu

Keyword(s):

Compressed Sensing ◽

Image Denoising ◽

Signal To Noise Ratio ◽

Sparse Recovery ◽

Coordinate Descent ◽

Sparse Signal ◽

Sparse Signal Recovery ◽

Signal Recovery ◽

Recovery Algorithm ◽

Core Concepts

Compressed sensing theory is widely used in the field of fault signal diagnosis and image processing. Sparse recovery is one of the core concepts of this theory. In this paper, we proposed a sparse recovery algorithm using a smoothed l0 norm and a randomized coordinate descent (RCD), then applied it to sparse signal recovery and image denoising. We adopted a new strategy to express the (P0) problem approximately and put forward a sparse recovery algorithm using RCD. In the computer simulation experiments, we compared the performance of this algorithm to other typical methods. The results show that our algorithm possesses higher precision in sparse signal recovery. Moreover, it achieves higher signal to noise ratio (SNR) and faster convergence speed in image denoising.

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A Fast Sparse Recovery Algorithm for Compressed Sensing Using Approximate l0 Norm and Modified Newton Method

Materials ◽

10.3390/ma12081227 ◽

2019 ◽

Vol 12 (8) ◽

pp. 1227 ◽

Cited By ~ 2

Author(s):

Dingfei Jin ◽

Yue Yang ◽

Tao Ge ◽

Daole Wu

Keyword(s):

Compressed Sensing ◽

Newton Method ◽

Simulation Experiment ◽

Sparse Recovery ◽

Signal Recovery ◽

Recovery Efficiency ◽

Recovery Algorithm ◽

Modified Newton Method ◽

Computer Simulation Experiment ◽

Recovery Algorithms

In this paper, we propose a fast sparse recovery algorithm based on the approximate l0 norm (FAL0), which is helpful in improving the practicability of the compressed sensing theory. We adopt a simple function that is continuous and differentiable to approximate the l0 norm. With the aim of minimizing the l0 norm, we derive a sparse recovery algorithm using the modified Newton method. In addition, we neglect the zero elements in the process of computing, which greatly reduces the amount of computation. In a computer simulation experiment, we test the image denoising and signal recovery performance of the different sparse recovery algorithms. The results show that the convergence rate of this method is faster, and it achieves nearly the same accuracy as other algorithms, improving the signal recovery efficiency under the same conditions.

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Block Compressed Sensing of Video Based on Variable Sampling Rates and Multihypothesis Predictions

JOURNAL OF ELECTRONICS INFORMATION TECHNOLOGY ◽

10.3724/sp.j.1146.2012.00590 ◽

2014 ◽

Vol 35 (1) ◽

pp. 203-208 ◽

Cited By ~ 1

Author(s):

Qiu-sheng Lian ◽

Tian Tian ◽

Shu-zhen Chen ◽

Wei Guo

Keyword(s):

Compressed Sensing ◽

Variable Sampling ◽

Block Compressed Sensing

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A Sparse Recovery Algorithm Based on Particle Swarm Optimization

JOURNAL OF ELECTRONICS INFORMATION TECHNOLOGY ◽

10.3724/sp.j.1146.2012.01397 ◽

2014 ◽

Vol 35 (11) ◽

pp. 2733-2738

Author(s):

Lu-feng Liu ◽

Xin-peng Du ◽

Li-zhi Cheng

Keyword(s):

Particle Swarm Optimization ◽

Particle Swarm ◽

Sparse Recovery ◽

Swarm Optimization ◽

Recovery Algorithm

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Ultrasonic Block Compressed Sensing Imaging Reconstruction Algorithm Based on Wavelet Sparse Representation

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405615666191209151746 ◽

2020 ◽

Vol 16 (3) ◽

pp. 262-272

Author(s):

Guangzhi Dai ◽

Zhiyong He ◽

Hongwei Sun

Keyword(s):

Compressed Sensing ◽

Sparse Representation ◽

Linear Array ◽

Imaging System ◽

Ultrasonic Imaging ◽

Large Data ◽

Reconstruction Algorithm ◽

Array Transducer ◽

Total Data ◽

Block Compressed Sensing

Background: This study is carried out targeting the problem of slow response time and performance degradation of imaging system caused by large data of medical ultrasonic imaging. In view of the advantages of CS, it is applied to medical ultrasonic imaging to solve the above problems. Objective: Under the condition of satisfying the speed of ultrasound imaging, the quality of imaging can be further improved to provide the basis for accurate medical diagnosis. Methods: According to CS theory and the characteristics of the array ultrasonic imaging system, block compressed sensing ultrasonic imaging algorithm is proposed based on wavelet sparse representation. Results: Three kinds of observation matrices have been designed on the basis of the proposed algorithm, which can be selected to reduce the number of the linear array channels and the complexity of the ultrasonic imaging system to some extent. Conclusion: The corresponding simulation program is designed, and the result shows that this algorithm can greatly reduce the total data amount required by imaging and the number of data channels required for linear array transducer to receive data. The imaging effect has been greatly improved compared with that of the spatial frequency domain sparse algorithm.

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Forensic analysis of beverage stains using hyperspectral imaging

Scientific Reports ◽

10.1038/s41598-021-85737-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Binu Melit Devassy ◽

Sony George

Keyword(s):

Hyperspectral Imaging ◽

Crime Scene ◽

Forensic Analysis ◽

Classification Model ◽

Support Vector ◽

Additional Information ◽

Non Invasive ◽

Spectral Bands ◽

Data Dimensionality Reduction ◽

Gradient Based

AbstractDocumentation and analysis of crime scene evidences are of great importance in any forensic investigation. In this paper, we present the potential of hyperspectral imaging (HSI) to detect and analyze the beverage stains on a paper towel. To detect the presence and predict the age of the commonly used drinks in a crime scene, we leveraged the additional information present in the HSI data. We used 12 different beverages and four types of paper hand towel to create the sample stains in the current study. A support vector machine (SVM) is used to achieve the classification, and a convolutional auto-encoder is used to achieve HSI data dimensionality reduction, which helps in easy perception, process, and visualization of the data. The SVM classification model was re-established for a lighter and quicker classification model on the basis of the reduced dimension. We employed volume-gradient-based band selection for the identification of relevant spectral bands in the HSI data. Spectral data recorded at different time intervals up to 72 h is analyzed to trace the spectral changes. The results show the efficacy of the HSI techniques for rapid, non-contact, and non-invasive analysis of beverage stains.

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