An efficient adaptive compressive sensing technique for underwater image compression in IoUT

Monika, R.; Dhanalakshmi, Samiappan; Kumar, R.; Narayanamoorthi, R.; Lai, Khin Wee

doi:10.1007/s11276-022-02921-1

Cited by 6 publications

(2 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[20]. R. Monika et al presented an efficient adaptive compressive sensing method for underwater imaging [21]. M.P.…”

Section: Related Workmentioning

confidence: 99%

ICRICS: Iterative Compensation Recovery for Image Compressive Sensing

Trocan

Sawan

et al. 2022

Preprint

View full text Add to dashboard Cite

Closed-loop architecture is widely utilized in automatic control systems and attains distinguished dynamic and static performance. However, classical compressive sensing systems employ an open-loop architecture with separated sampling and reconstruction units. Therefore, a method of iterative compensation recovery for image compressive sensing (ICRICS) is proposed by introducing a closed-loop framework into traditional compressive sensing systems. The proposed method depends on any existing approaches and upgrades their reconstruction performance by adding a negative feedback structure. Theoretical analysis of the negative feedback of compressive sensing systems is performed. An approximate mathematical proof of the effectiveness of the proposed method is also provided. Simulation experiments on more than 3 image datasets show that the proposed method is superior to 10 competing approaches in reconstruction performance. The maximum increment of the average peak signal-to-noise ratio is 4.36 dB, and the maximum increment of the average structural similarity is 0.034 based on one dataset. The proposed method based on a negative feedback mechanism can efficiently correct the recovery error in the existing image compressive sensing systems.

show abstract

“…[20]. R. Monika et al presented an efficient adaptive compressive sensing method for underwater imaging [21]. M.P.…”

Section: Related Workmentioning

confidence: 99%

ICRICS: Iterative Compensation Recovery for Image Compressive Sensing

Trocan

Sawan

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Furthermore, the traditional big data processing methods (Cao et al, 2018) that rely on statistical properties lack generalization ability. JPEG and other traditional compression algorithms have limitations regarding reconstruction quality, data rate, and compression performance, making them unsuitable for resource-constrained IoUT (Monika et al, 2022b).…”

Section: Introductionmentioning

confidence: 99%

ESPC-BCS-Net: A network-based CS method for underwater image compression and reconstruction

Chen²,

Yu³

2023

Front. Mar. Sci.

View full text Add to dashboard Cite

The Internet of Underwater Things (IoUT) is a typical energy-limited and bandwidth-limited system where the technical bottleneck is the asymmetry between the massive demand for information access and the limited communication bandwidth. Therefore, storing and transmitting high-quality underwater images is a challenging task. The data measured by cameras need to be effectively compressed before transmission to reduce storage and reconstruc-ted with minor errors, which is the best solution. Compressed sensing (CS) theory breaks through the Nyquist sampling theorem and has been widely used to reconstruct sparse signals accurately. For adaptive sampling underwater images and improving the reconstruction performance, we propose the ESPC-BCS-Net by combining the advantages of CS and Deep Learning. The ESPC-BCS-Net consists of three parts: Sampling-Net, ESPC-Net, and BCS-Net. The parameters (e.g. sampling matrix, sparse transforms, shrinkage thresholds, etc.) in ESPC-BCS-Net are learned end-to-end rather than hand-crafted. The Sampling-Net achieves adaptive sampling by replacing the sampling matrix with a convolutional layer. The ESPC-Net implements image upsampling, while the BCS-Net is used to image reconstruction. The efficient sub-pixel layer of ESPC-Net effectively avoids blocking artifacts. The visual and quantitative evaluation of the experimental results shows that the underwater image reconstruction still performs well when the CS ratio is 0.1 and the PSNR of the reconstructed underwater images is above 29.

show abstract

An optimal adaptive reweighted sampling-based adaptive block compressed sensing for underwater image compression

Monika,

Dhanalakshmi

2023

Vis Comput

View full text Add to dashboard Cite

An efficient adaptive compressive sensing technique for underwater image compression in IoUT

Cited by 6 publications

References 30 publications

ICRICS: Iterative Compensation Recovery for Image Compressive Sensing

ICRICS: Iterative Compensation Recovery for Image Compressive Sensing

ESPC-BCS-Net: A network-based CS method for underwater image compression and reconstruction

An optimal adaptive reweighted sampling-based adaptive block compressed sensing for underwater image compression

Contact Info

Product

Resources

About