Compressive Sensing Measurements Research Articles

Operational Support Estimator Networks.

In this work, we propose a novel approach called Operational Support Estimator Networks (OSENs) for the support estimation task. Support Estimation (SE) is defined as finding the locations of non-zero elements in sparse signals. By its very nature, the mapping between the measurement and sparse signal is a non-linear operation. Traditional support estimators rely on computationally expensive iterative signal recovery techniques to achieve such non-linearity. Contrary to the convolutional layers, the proposed OSEN approach consists of operational layers that can learn such complex non-linearities without the need for deep networks. In this way, the performance of non-iterative support estimation is greatly improved. Moreover, the operational layers comprise so-called generative super neurons with non-local kernels. The kernel location for each neuron/feature map is optimized jointly for the SE task during training. We evaluate the OSENs in three different applications: i. support estimation from Compressive Sensing (CS) measurements, ii. representation-based classification, and iii. learning-aided CS reconstruction where the output of OSENs is used as prior knowledge to the CS algorithm for enhanced reconstruction. Experimental results show that the proposed approach achieves computational efficiency and outperforms competing methods, especially at low measurement rates by significant margins. The software implementation is shared at https://github.com/meteahishali/OSEN.

Transformer based Douglas-Rachford unrolling network for compressed sensing

Compressed sensing (CS) with the binary sampling matrix is hardware-friendly and memory-saving in the signal processing field. Existing Convolutional Neural Network (CNN)-based CS methods show potential restrictions in exploiting non-local similarity and lack interpretability. In parallel, the emerging Transformer architecture performs well in modelling long-range correlations. To further improve the CS reconstruction quality from highly under-sampled CS measurements, a Transformer based deep unrolling reconstruction network abbreviated as DR-TransNet is proposed, whose design is inspired by the traditional iterative Douglas-Rachford algorithm. It combines the merits of structure insights of optimization-based methods and the speed of the network-based ones. Therein, a U-type Transformer based proximal sub-network is elaborated to reconstruct images in the wavelet domain and the spatial domain as an auxiliary mode, which aims to explore local informative details and global long-term interaction of the images. Specially, a flexible single model is trained to address the CS reconstruction with different binary CS sampling ratios. Compared with the state-of-the-art CS reconstruction methods with the binary sampling matrix, the proposed method can achieve appealing improvements in terms of Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM) and visual metrics. Codes are available at https://github.com/svyueming/DR-TransNet.

Volatility-based diversity awareness for distributed data storage of Mobile Crowd Sensing

With the widespread utilization of sensors and the rapid development of Mobile Crowd Sensing (MCS), the framework of distributed data storage, which makes use of the limited storage capacity of sensing devices, is providing an alternative to cloud-based data storage. Since the construction of the distributed storage framework for MCS has to be completed by Compressed Sensing (CS) theory, a reasonable measurement allocation becomes a no-brainer if we want to obtain more recovery precision of data without wasting measurement resources. To more efficiently address the above problems, we present a novel strategy of Volatility-Based Diversity Awareness for CS measurement allocation called VBDA, which collectively achieves lower computational complexity, adaptive sampling rate allocation for non-visual data, and high-quality reconstruction. We first process the target monitoring region in blocks. Then, we calculate the degree of data diversity of each area using the volatility, which is used to assess the importance of the different areas. Worthy note, based on the volatility calculation, we can obtain the magnitude of the variation in diversity that is only present in the real data, even when using very ambiguous recovered data. This is primarily due to the volatility unique design concept, which attempts to offset partially identical errors in adjacent recovered data using volatility calculations. Finally, to rationally allocate measurement resources, a volatility-based sampling rate allocation scheme is proposed. We further provide an implementation for practical deployments of VBDA in various related contexts. Numerous experimental show that the VBDA performs excellently. In comparison to the current state-of-the-art strategy, it improves data recovery accuracy by 12.2% without the use of any prior knowledge, while being adaptable to many different distribution types of data.

Design, implementation, and analysis of a compressed sensing photoacoustic projection imaging system.

Compressed sensing (CS) uses special measurement designs combined with powerful mathematical algorithms to reduce the amount of data to be collected while maintaining image quality. This is relevant to almost any imaging modality, and in this paper we focus on CS in photoacoustic projection imaging (PAPI) with integrating line detectors (ILDs). Our previous research involved rather general CS measurements, where each ILD can contribute to any measurement. In the real world, however, the design of CS measurements is subject to practical constraints. In this research, we aim at a CS-PAPI system where each measurement involves only a subset of ILDs, and which can be implemented in a cost-effective manner. We extend the existing PAPI with a self-developed CS unit. The system provides structured CS matrices for which the existing recovery theory cannot be applied directly. A random search strategy is applied to select the CS measurement matrix within this class for which we obtain exact sparse recovery. We implement a CS PAPI system for a compression factor of 4:3, where specific measurements are made on separate groups of 16 ILDs. We algorithmically design optimal CS measurements that have proven sparse CS capabilities. Numerical experiments are used to support our results. CS with proven sparse recovery capabilities can be integrated into PAPI, and numerical results support this setup. Future work will focus on applying it to experimental data and utilizing data-driven approaches to enhance the compression factor and generalize the signal class.

Atrial Fibrillation Detection from Compressed ECG Measurements for Wireless Body Sensor Network

Recent years have witnessed an increasing prevalence of wearable devices in the public, where atrial fibrillation (AF) detection is a popular application in these devices. Generally, AF detection is performed on cloud whereas this paper describes an on-device AF detection method. Technically, compressed sensing (CS) is first used for electrocardiograph (ECG) acquisition. Then QRS detection is proposed to be performed directly on the compressed CS measurements, rather than on the reconstructed signals on the powerful cloud server. Based on the extracted QRS information, AF is determined by quantitatively analyzing the ( RR , dRR ) plot. Databases with ECG samples collected from both medical-level (MIT-BIH afdb) and wearable ECG devices (Physionet Challenge 2017) are introduced for performance validation. The experiment results well demonstrate that our on-device AF detection algorithm can approach the performance of those implemented on the raw signals. Our proposal is suitable for AF screening directly on the wearable devices, without the support of the data center for signal reconstruction and intelligent analysis.

CS-GAC: Compressively sensed geodesic active contours

This paper proposes an edge based compressively sensed (CS) geodesic active contour (GAC) model, termed CS-GAC, to ensure faithful edge detection and accurate object segmentation. The motivation behind this paper is that edge information driving the contour evolution can be iteratively obtained by incomplete CS measurements. In each iteration, the CS-GAC is a three-step process including edge detection, active contouring and sparse reconstruction. Instead of working on the final reconstructed images themselves, the evolution of the CS-GAC is driven by a few CS measurements and guided by updatable edge information. The edge information is generated by a complex shearlet transform (CST) based edge map. In the framework, reconstruction and edge detection work alternately. The iterative update property that takes advantages of both edge sparsity and edge detection can largely improve the evolution precision. Numerical experiments show that the CS-GAC can obtain challenging segmentation results in comparisons with the state of the art methods, and has competitive prospects.

Adaptive LiDAR Reconstruction by Convolutional Compressive Sensing Network and Multivariate Empirical Mode Decomposition

With the recent advancement of LiDAR data processing pipelines integrated with signal processing techniques, it is now possible to rapidly acquire, process, and infer multidimensional information of 3D surroundings. This work proposes a novel compressive sensing (CS) framework (named MEMD-CSNet), based on Multivariate Empirical Mode Decomposition (MEMD) for adaptive 3D reconstruction of airborne urban LiDAR point clouds. EMD is a data-driven transform for decomposing the input data into Intrinsic Mode Functions (IMFs), capturing the implicit features in terms of variation in spatial frequency. Our approach generates a new filtered 3D signal constituting an optimal subset of IMFs based on the understanding of their statistical significance. Further, this filtered signal hierarchically extracts the overall shape in the last IMF (Residual function). Our proposed MEMD-CSNet is a deep convolutional network that (1) computes a multivariate EMD on input raw 3D LiDAR point clouds for generating data-dependent feature-rich filtered signal and (2) additionally augments this signal with convolutional features, thus learning a data-centric representation function (Ψ) for transforming the input into a low-dimensional sparse basis. We present extensive qualitative and quantitative analysis using the proposed MEMD-CSNet for a set of chosen CS measurement ratios. MEMD-CSNet achieves a maximum reconstruction PSNR of 108.22 dB (approx. 30 dB/1.36x better than without using MEMD) for reconstruction fidelity on selected urban LiDAR scenes. We envisage that our proposed MEMD-CSNet has a high potential (as a data-centric framework) for generating data-driven sparse feature maps and reconstructing entire 3D LiDAR point clouds from a few sparse measurements.

Privacy-preserving image compressed sensing by embedding a controllable noise-injected transformation for IoT devices

Recently, compressed sensing (CS) based cryptosystem has received extensive attention in information security field. However, this cryptosystem cannot resist known-plaintext attack (KPA) under the secret key multi-time-using (MTU) scenario because of its linear sampling process. To address this concern, a privacy-preserving image CS scheme is proposed, which embeds a controllable noise-injected transformation (CNT) into the linear sampling process of CS to resist KPA. First, a CNT operation is applied to pre-encrypt the image. Second, the pre-encrypted image is re-encrypted and compressed by using CS at the same time. Third, the final CS measurements are quantized into bits. Since the embedding of CNT operation destroys the linearity of CS, the proposed cryptosystem can resist the existing KPA method under the secret key MTU scenario. Lastly, an iterative total variation (TV) algorithm based on ADMM framework, called TV-ADMM, is proposed for image recovery. Simulation results demonstrate that the proposed cryptosystem significantly enhances the security of the CS-based cryptosystem with slightly sacrificing the compression performance in high compression rate cases.

Deep Convolutional Compressed Sensing-Based Adaptive 3D Reconstruction of Sparse LiDAR Data: A Case Study for Forests

LiDAR point clouds are characterized by high geometric and radiometric resolution and are therefore of great use for large-scale forest analysis. Although the analysis of 3D geometries and shapes has improved at different resolutions, processing large-scale 3D LiDAR point clouds is difficult due to their enormous volume. From the perspective of using LiDAR point clouds for forests, the challenge lies in learning local and global features, as the number of points in a typical 3D LiDAR point cloud is in the range of millions. In this research, we present a novel end-to-end deep learning framework called ADCoSNet, capable of adaptively reconstructing 3D LiDAR point clouds from a few sparse measurements. ADCoSNet uses empirical mode decomposition (EMD), a data-driven signal processing approach with Deep Learning, to decompose input signals into intrinsic mode functions (IMFs). These IMFs capture hierarchical implicit features in the form of decreasing spatial frequency. This research proposes using the last IMF (least varying component), also known as the Residual function, as a statistical prior for capturing local features, followed by fusing with the hierarchical convolutional features from the deep compressive sensing (CS) network. The central idea is that the Residue approximately represents the overall forest structure considering it is relatively homogenous due to the presence of vegetation. ADCoSNet utilizes this last IMF for generating sparse representation based on a set of CS measurement ratios. The research presents extensive experiments for reconstructing 3D LiDAR point clouds with high fidelity for various CS measurement ratios. Our approach achieves a maximum peak signal-to-noise ratio (PSNR) of 48.96 dB (approx. 8 dB better than reconstruction without data-dependent transforms) with reconstruction root mean square error (RMSE) of 7.21. It is envisaged that the proposed framework finds high potential as an end-to-end learning framework for generating adaptive and sparse representations to capture geometrical features for the 3D reconstruction of forests.

IEF-CSNET: Information Enhancement and Fusion Network for Compressed Sensing Reconstruction.

The rapidly growing requirement for data has put forward Compressed Sensing (CS) to realize low-ratio sampling and to reconstruct complete signals. With the intensive development of Deep Neural Network (DNN) methods, performance in image reconstruction from CS measurements is constantly increasing. Currently, many network structures pay less attention to the relevance of before- and after-stage results and fail to make full use of relevant information in the compressed domain to achieve interblock information fusion and a great receptive field. Additionally, due to multiple resamplings and several forced compressions of information flow, information loss and network structure redundancy inevitably result. Therefore, an Information Enhancement and Fusion Network for CS reconstruction (IEF-CSNET) is proposed in this work, and a Compressed Information Extension (CIE) module is designed to fuse the compressed information in the compressed domain and greatly expand the receptive field. The Error Comprehensive Consideration Enhancement (ECCE) module enhances the error image by incorporating the previous recovered error so that the interlink among the iterations can be utilized for better recovery. In addition, an Iterative Information Flow Enhancement (IIFE) module is further proposed to complete the progressive recovery with loss-less information transmission during the iteration. In summary, the proposed method achieves the best effect, exhibits high robustness at this stage, with the peak signal-to-noise ratio (PSNR) improved by 0.59 dB on average under all test sets and sampling rates, and presents a greatly improved speed compared with the best algorithm.

Video Transmission Secrecy Improvement Based on Fractional Order Hyper Chaotic System

In the Digital World scenario, the confidentiality of information in video transmission plays an important role. Chaotic systems have been shown to be effective for video signal encryption. To improve video transmission secrecy, compressive encryption method is proposed to accomplish compression and encryption based on fractional order hyper chaotic system that incorporates Compressive Sensing (CS), pixel level, bit level scrambling and nucleotide Sequences operations. The measurement matrix generates by the fractional order hyper chaotic system strengthens the efficiency of the encryption process. To avoid plain text attack, the CS measurement is scrambled to its pixel level, bit level scrambling decreases the similarity between the adjacent measurements and the nucleotide sequence operations are done on the scrambled bits, increasing the encryption. Two stages are comprised in the reconstruction technique, the first stage uses the intra-frame similarity and offers robust preliminary retrieval for each frame, and the second stage iteratively improves the efficiency of reconstruction by integrating inter frame Multi Hypothesis (MH) estimation and weighted residual sparsity modeling. In each iteration, the residual coefficient weights are modified using a mathematical approach based on the MH predictions, and the Split Bregman iteration algorithm is defined to resolve weighted l1 regularization. Experimental findings show that the proposed algorithm provides good compression of video coupled with an efficient encryption method that is resistant to multiple attacks.

From Patch to Pixel: A Transformer-Based Hierarchical Framework for Compressive Image Sensing

The convolutional neural network (CNN)-based reconstruction methods have dominated the compressive sensing (CS) in recent years. However, existing CNN-based approaches show potential restrictions in capturing non-local similarity of images, because of the intrinsic characteristic of convolutional layers, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathit{i.e.}$</tex-math></inline-formula> , locality and weight sharing. In parallel, the emerging Transformer architecture shows fine capacity in modeling long-distance correlations onto embedded tokens for language and images. Yet vanilla Transformer does not exceed CNN-based networks considerably but shows roughly comparable performance, and the culprit can be the missing of sophisticated inductive bias regarding the local image structures. In this article, to eliminate the restrictions of the aforementioned paradigms, we propose a Transformer-based hierarchical framework, dubbed TCS-Net, for compressive image sensing (or image compressive sensing) with a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathit{patch-to-pixel}$</tex-math></inline-formula> manner. Concretely, the proposed TCS-Net consists of an image acquisition module and a reconstruction module (includes two key decoding phases: a patch-wise decoding phase and a pixel-wise decoding phase). The acquisition module can implement data-driven image sampling by jointly learning with the decoding phases. By adjusting the Transformer architecture to the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathit{patch-to-pixel}$</tex-math></inline-formula> multi-stage pattern, our reconstruction module can gradually decode the CS measurements from the patch-wise outlines to the pixel-wise textures, thereby building a high-precision mapping for image reconstruction. Extensive experiments on several datasets verify that the proposed TCS-Net outperforms existing state-of-the-art image CS methods by considerable margins.

Convergence rates for the joint solution of inverse problems with compressed sensing data

Compressed sensing (CS) is a powerful tool for reducing the amount of data to be collected while maintaining high spatial resolution. Such techniques work well in practice and at the same time are supported by solid theory. Standard CS results assume measurements to be made directly on the targeted signal. In many practical applications, however, CS information can only be taken from indirect data related to the original signal by an additional forward operator. If inverting the forward operator is ill-posed, then existing CS theory is not applicable. In this paper, we address this issue and present two joint reconstruction approaches, namely relaxed co-regularization and strict co-regularization, for CS from indirect data. As main results, we derive error estimates for recovering and . In particular, we derive a linear convergence rate in the norm for the latter. To obtain these results, solutions are required to satisfy a source condition and the CS measurement operator is required to satisfy a restricted injectivity condition. We further show that these conditions are not only sufficient but even necessary to obtain linear convergence.

Application of Compressive Sensing in the Presence of Noise for Transient Photometric Events

Compressive sensing is a simultaneous data acquisition and compression technique, which can significantly reduce data bandwidth, data storage volume, and power. We apply this technique for transient photometric events. In this work, we analyze the effect of noise on the detection of these events using compressive sensing (CS). We show numerical results on the impact of source and measurement noise on the reconstruction of transient photometric curves, generated due to gravitational microlensing events. In our work, we define source noise as background noise, or any inherent noise present in the sampling region of interest. For our models, measurement noise is defined as the noise present during data acquisition. These results can be generalized for any transient photometric CS measurements with source noise and CS data acquisition measurement noise. Our results show that the CS measurement matrix properties have an effect on CS reconstruction in the presence of source noise and measurement noise. We provide potential solutions for improving the performance by tuning some of the properties of the measurement matrices. For source noise applications, we show that choosing a measurement matrix with low mutual coherence can lower the amount of error caused due to CS reconstruction. Similarly, for measurement noise addition, we show that by choosing a lower expected value of the binomial measurement matrix, we can lower the amount of error due to CS reconstruction.

Review on Compressive Sensing Algorithms for ECG Signal for IoT Based Deep Learning Framework

Nowadays, healthcare is becoming very modern, and the support of Internet of Things (IoT) is inevitable in a personal healthcare system. A typical personal healthcare system acquires vital parameters from human users and stores them in a cloud platform for further analysis. Acquiring fundamental biomedical signal, such as with the Electrocardiograph (ECG), is also considered for specific disease analysis in personal healthcare systems. When such systems are scaled up, there is a heavy demand for internet channel capacity to accommodate real time seamless flow of discrete samples of biomedical signals. So, there is a keen need for real time data compression of biomedical signals. Compressive Sensing (CS) has recently attracted more interest due to its compactness and its feature of the faithful reconstruction of signals from fewer linear measurements, which facilitates less than Shannon’s sampling rate by exploiting the signal sparsity. The most common biomedical signal that is to be analyzed is the ECG signal, as the prediction of heart failure at an early stage can save a human life. This review is for a vast use-case of IoT framework in which CS measurements of ECG are acquired, communicated through Internet to a server, and the arrhythmia are analyzed using Machine learning (ML). Assuming this use-case specific for ECG, in this review many technical aspects are considered regarding various research components. The key aspect is on the investigation of the best sensing method, and to address this, various sensing matrices are reviewed, analyzed and recommended. The next aspect is the selection of the optimal sparsifying method, and the review recommends unexplored ECG compression algorithms as sparsifying methods. The other aspects are optimum reconstruction algorithms, best hardware implementations, suitable ML methods and effective modality of IoT. In this review all these components are considered, and a detailed review is presented which enables us to orchestrate the use-case specified above. This review focuses on the current trends in CS algorithms for ECG signal compression and its hardware implementation. The key to successful reconstruction of the CS method is the right selection of sensing and sparsifying matrix, and there are many unexplored sparsifying methods for the ECG signal. In this review, we shed some light on new possible sparsifying techniques. A detailed comparison table of various CS algorithms, sensing matrix, sparsifying techniques with different ECG dataset is tabulated to quantify the capability of CS in terms of appropriate performance metrics. As per the use-case specified above, the CS reconstructed ECG signals are to be subjected to ML analysis, and in this review the compressive domain inference approach is discussed. The various datasets, methodologies and ML models for ECG applications are studied and their model accuracies are tabulated. Mostly, the previous research on CS had studied the performance of CS using numerical simulation, whereas there are some good attempts for hardware implementations for ECG applications, and we studied the uniqueness of each method and supported the study with a comparison table. As a consolidation, we recommend new possibilities of the research components in terms of new transforms, new sparsifying methods, suggestions for ML approaches and hardware implementation.

Compressive Domain Deep CNN for Image Classification and Performance Improvement Using Genetic Algorithm-Based Sensing Mask Learning

The majority of digital images are stored in compressed form. Generally, image classification using convolution neural network (CNN) is done in uncompressed form rather than compressed one. Training the CNN in the compressed domain eliminates the requirement for decompression process and results in improved efficiency, minimal storage, and lesser cost. Compressive sensing (CS) is one of the effective and efficient method for signal acquisition and recovery and CNN training on CS measurements makes the entire process compact. The most popular sensing phenomenon used in CS is based on image acquisition using single pixel camera (SPC) which has complex design implementation and usually a matrix simulation is used to represent the SPC process in numerical demonstration. The CS measurements using this phenomenon are visually different from the image and to add this in the training set of the compressed learning framework, there is a need for an inverse SPC process that is to be applied all through the training and testing dataset image samples. In this paper we proposed a simple sensing phenomenon which can be implemented using the image output of a standard digital camera by retaining few pixels and forcing the rest of the pixels to zero and this reduced set of pixels is assumed as CS measurements. This process is modeled by a binary mask application on the image and the resultant image still subjectively legible for human vision and can be used directly in the training dataset. This sensing mask has very few active pixels at arbitrary locations and there is a lot of scope to heuristically learn the sensing mask suitable for the dataset. Only very few attempts had been made to learn the sensing matrix and the sole effect of this learning process on the improvement of CNN model accuracy is not reported. We proposed to have an ablation approach to study how this sensing matrix learning improves the accuracy of the basic CNN architecture. We applied CS for two class image dataset by applying a Primitive Walsh Hadamard (PWH) binary mask function and performed the classification experiment using a basic CNN. By retaining arbitrary amount of pixel in the training and testing dataset we applied CNN on the compressed measurements to perform image classification and studied and reported the model performance in terms of training and validation accuracies by varying the amount of pixels retained. A novel Genetic Algorithm-based compressive learning (GACL) method is proposed to learn the PWH mask to optimize the model training accuracy by using two different crossover techniques. In the experiment conducted for the case of compression ratio (CR) 90% by retaining only 10% of the pixels in every images both in training and testing dataset that represent two classes, the training accuracy is improved from 67% to 85% by using diagonal crossover in offspring creation of GACL. The robustness of the method is examined by applying GACL for user defined multiclass dataset and achieved better CNN model accuracies. This work will bring out the strength of sensing matrix learning which can be integrated with advanced training models to minimize the amount of information that is to be sent to central servers and will be suitable for a typical IoT frame work.

Secure sparse watermarking on DWT-SVD for digital images

This paper suggests a sparse watermark image embedding scheme on the singular values of the pre-selected wavelet sub-band coefficients of the digital images. Dictionary learning (DL) method is used to make the watermark image sparse. Then a watermark decoder is designed based on the theory of compressed sensing (CS) in the framework of alternating direction method of multiplier (ADMM). The integration of DL and CS offers two benefits, firstly DL combined with singular value decomposition (SVD), enables protection against false positive detection (FPD) where the sparse watermark enhances embedding capacity at high imperceptibility of the hidden data. Simulation results show high visual quality for the watermarked images (PSNR = 60.45 dB, SSIM = 0.9997 and FSIM = 0.9979) and robustness (NCC value for the decoded watermark is ∼ 0.9) against various image processing operations including an unauthorized decoding - a typical value of 85% CS measurements lead to a watermarked image results in a FPD value of 0.42 that shows a visually indistinguishable watermark.

Secure Sampling and Low-Overhead Compressive Analysis by Linear Transformation

Compressed sensing (CS) is attractive in wireless multimedia sensor networks (WMSNs) because it achieves sampling-compression and confidentiality protection simultaneously. Moreover, applications in WMSNs emphasize the requirement to process real-time data rapidly. However, the reconstruction of CS has high memory and computational overhead, which prevents it from being used online on resource-limited edge devices. In order to effectively analyze the received CS measurements, a low-overhead compressive analysis framework based on linear transformation is proposed. The linear transformation is used to fast construct a proxy that approximates the original signal from CS measurements for inferences online, and the transformation matrix is built by collaborative learning off-line. In addition, we introduce a hardware-friendly binary matrix to update the measurement matrix to ensure the security of the sampling framework. Both theoretical and experimental analyses validate the low overhead and confidentiality of the proposed compressive analysis framework.

Ship Detection Based on Compressive Sensing Measurements of Optical Remote Sensing Scenes

The compressive sensing (CS)-based optical remote sensing (ORS) imaging system have been verified the feasibility of through numerical simulation experiments, which can reduce the demand for sampling equipment, effectively reduce sampling data, save storage space, and reduce transmission costs. However, it needs to reconstruct the original scene when facing the task of ship detection. The scene reconstruction process of CS is computationally expensive, high memory demanding, and time-consuming. In response to this problem, this paper proposes an innovation pipeline to perform ship detection tasks, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , directly performing ship detection on CS measurements obtained by the imaging system, which avoids the process of scene reconstruction. To achieve the ship detection of CS measurements in the pipeline, we design a CNN-based algorithm, CS-CenterNet, which jointly optimizes the scene compression sampling phase and the measurements' ship detection phase. CS-CenterNet is divided into convolution measurement layer (CML), optimized hourglass network (OHgN), and optimized three-branch head network (OTBHN). Firstly, CML without bias or activation function simulates the block compression sampling process in CS-based ORS imaging system, which performs convolutional coding on the scene to obtain the measurements. Secondly, OHgN extracts high-resolution feature information of measurements. Finally, OTBHN performs heat-map prediction, center-point offset prediction, and width-height prediction. We test the performance of CS-CenterNet using the HRSC2016 and LEVIR datasets. The experimental results show that the algorithm can achieve high-accuracy ship detection based on CS measurements of ORS scenes.

TransCL: Transformer Makes Strong and Flexible Compressive Learning.

Compressive learning (CL) is an emerging framework that integrates signal acquisition via compressed sensing (CS) and machine learning for inference tasks directly on a small number of measurements. It can be a promising alternative to classical image-domain methods and enjoys great advantages in memory saving and computational efficiency. However, previous attempts on CL are not only limited to a fixed CS ratio, which lacks flexibility, but also limited to MNIST/CIFAR-like datasets and do not scale to complex real-world high-resolution (HR) data or vision tasks. In this article, a novel transformer-based compressive learning framework on large-scale images with arbitrary CS ratios, dubbed TransCL, is proposed. Specifically, TransCL first utilizes the strategy of learnable block-based compressed sensing and proposes a flexible linear projection strategy to enable CL to be performed on large-scale images in an efficient block-by-block manner with arbitrary CS ratios. Then, regarding CS measurements from all blocks as a sequence, a pure transformer-based backbone is deployed to perform vision tasks with various task-oriented heads. Our sufficient analysis presents that TransCL exhibits strong resistance to interference and robust adaptability to arbitrary CS ratios. Extensive experiments for complex HR data demonstrate that the proposed TransCL can achieve state-of-the-art performance in image classification and semantic segmentation tasks. In particular, TransCL with a CS ratio of 10% can obtain almost the same performance as when operating directly on the original data and can still obtain satisfying performance even with an extremely low CS ratio of 1%. The source codes of our proposed TransCL is available at https://github.com/MC-E/TransCL/.