Image Reconstruction Based on Compressive Sensing Using Total Variation Spatial Regulation for Microwave Imaging

Razzak, Izra Halim; Rizkinia, Mia; Basari, Basari

doi:10.1109/piers-spring46901.2019.9017774

Cited by 1 publication

(5 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The reconstruction result of this real phantom has a very high error because the input measurement signal is taken by a combination of twenty translations and three antenna rotations. Meanwhile, as mentioned in Section 2.2.1, the experimental results in previous research (Razzak et al, 2019) demonstrate the best results occur in a combination of forty translations and three antenna rotations.…”

Section: Microwave Image Reconstruction Performancementioning

confidence: 70%

“…Then, various translations and rotations of the two-dipole antennas were conducted to obtain a collection of measurement signal values to be reconstructed into an image. Previous research (Razzak et al, 2019) has shown that a combination of forty translations and three rotations gives the best reconstruction results of measurement data from software simulation. Therefore, we use these data to test qualitatively and quantitatively.…”

Section: Framework Implementation and Evaluationmentioning

confidence: 99%

“…The more similar and the smaller the error of the reconstructed image to the reference image/expected image, the better the reconstruction method. We compared the reconstruction results with other algorithms like SART, sparse CS and CS with total variation (TV) (Razzak et al, 2019). In qualitative analysis, the reconstructed images are compared based on visual appearance and brightness, contrasts and shape.…”

Section: Implementation and Evaluationmentioning

confidence: 99%

“…The CS was added with the TV method (Razzak et al, 2019), and the SLR−CS method was applied to the signal measurement shown in Figure 1. The microwaves are captured straight between a transmitter antenna and a receiver antenna after passing the object.…”

Section: Future Workmentioning

confidence: 99%

“…The data also tend to be sparse in a frequency domain, that is having many elements containing zero value among the total data set (Donoho et al, 2016;Basari and Ramdani, 2019). Previous research proposed CS with total variation (TV) regularization solved by the alternating direct method of multipliers (ADMM) (Razzak et al, 2019). The method shows a smoother reconstructed image and lower mean square error (MSE) than SART and sparse CS.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Design of Microwave-based Brain Tumor Detection Framework with the Development of Sparse and Low-Rank Compressive Sensing Image Reconstruction

Sholeh

Rizkinia²,

Basari³

2020

IJTech

Self Cite

View full text Add to dashboard Cite

Cancer is one of the leading causes of death, and the brain is one of the body's cancerprone organs. The early detection of brain tumors can reduce cancer risk, which is practically assisted and conducted using scanners such as computed tomography (CT) and magnetic resonance imaging (MRI). However, those modalities are high-cost and large-sized, and they have a side effect risk to health. Alternatively, microwave imaging offers a novel cancer scanning method for early detection with low cost, small size and low health risk. Consequently, this research designs and creates a framework with a novel microwave image reconstruction algorithm inside. The framework is a component of the controller and image reconstructor for a portable microwavebased brain tumor detector that is open source and multi-platform. For the novel algorithm, this research proposes a CS-based imaging algorithm by exploiting the data's sparse and low-rank properties. The experiment shows that the proposed algorithm can give better qualitative and quantitative reconstruction results compared to a full-sampling-based as well as CS-based algorithm.

show abstract

Section: Microwave Image Reconstruction Performancementioning

confidence: 70%

Section: Framework Implementation and Evaluationmentioning

confidence: 99%

Section: Implementation and Evaluationmentioning

confidence: 99%

Section: Future Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations