Adaptive High-Resolution Imaging Method Based on Compressive Sensing

Wang, Zijiao; Gao, Yufeng; Duan, Xinbin; Cao, Jingya

doi:10.3390/s22228848

Cited by 5 publications

(3 citation statements)

References 20 publications

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“…Although some traditional methods have been widely applied practically, such as wireless sensor network [ 17 ], hyperspectral imaging [ 18 ], Magnetic Resonance Imaging (MRI) [ 19 , 20 ], underwater sensors [ 21 ], and aerospace [ 22 ], they usually suffer from too slow a running speed, due to the inevitable large numbers of iterations and heavy matrix operations, which must be executed in a nonparallel way in each iteration. In addition, it is difficult to draw enough prior knowledge from big data, causing performance bottlenecks.…”

Section: Related Workmentioning

confidence: 99%

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

Zhou

Liu

Shen

2023

Sensors

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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.

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Section: Related Workmentioning

confidence: 99%

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

Zhou

Liu

Shen

2023

Sensors

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“…In addition, there are numerous studies comparing compressed detection algorithms to assess their performance under various conditions [27][28][29]. The use of CS in single pixel detectors has also been simulated to achieve high-resolution images [30,31]. Furthermore, the integration of deep learning techniques with compressed detection has gained attention in recent years.…”

Section: Related Workmentioning

confidence: 99%

Compressed Sensing Techniques Applied to Medical Images Obtained with Magnetic Resonance

Herguedas-Alonso,

García-Suárez,

Fernández-Martínez

2023

Mathematics

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The fast and reliable processing of medical images is of paramount importance to adequately generate data to feed machine learning algorithms that can prevent and diagnose health issues. Here, different compressed sensing techniques applied to magnetic resonance imaging are benchmarked as a means to reduce the acquisition time spent in the collection of data and signals that form the image. It is shown that by using these techniques, it is possible to reduce the number of signals needed and, therefore, substantially decrease the time to acquire the measurements. To this end, different algorithms are considered and compared: the iterative re-weighted least squares, the iterative soft thresholding algorithm, the iterative hard thresholding algorithm, the primal dual algorithm and the log barrier algorithm. Such algorithms have been implemented in different analysis programs that have been used to perform the reconstruction of the images, and it was found that the iterative soft thresholding algorithm gives the optimal results. It is found that the images obtained with this algorithm have lower quality than the original ones, but in any case, the quality should be good enough to distinguish each body structure and detect any health problems under an expert evaluation and/or statistical analysis.

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“…The authors demonstrated a low-cost, temperature-stabilized IR microscopy concept but did not report any assessment of the quality of the reconstructed images. An active imaging system that combined an array of SPCs was proposed by Z. Wang et al [33]. Through simulation, they demonstrated that adaptive sampling can produce a high-resolution image with a large field of view and rapid reconstruction.…”

Section: Introductionmentioning

confidence: 99%

Compressive Sensing Based Active Imaging System Using Programable Coded Mask and a Photodiode

2023

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There has been significant progress in the field of compressive sensing (CS) and its applications in various fields in recent years. Here, we have developed a CS-based active imaging system that does not require a pixelated array detector but instead uses a transmissive spatial light modulator as a programmable coded mask. Single-pixel cameras (SPCs) have many applications in fields such as terahertz (THz) and infrared (IR) imaging. With an increase in the number of pixels, array detectors can become expensive, especially in the THz and IR regimes. In this article, we have explored the concept of active illumination, which could be helpful in developing SPCs. The article focuses on the development of an active SPC, and the analysis of how coded masks affect the quality of the reconstructed image.

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Adaptive High-Resolution Imaging Method Based on Compressive Sensing

Cited by 5 publications

References 20 publications

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

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

Compressed Sensing Techniques Applied to Medical Images Obtained with Magnetic Resonance

Compressive Sensing Based Active Imaging System Using Programable Coded Mask and a Photodiode

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