Phase Unwrapping by Markov Chain Monte Carlo Energy Minimization

Adi, Kusworo; Suksmono, Andriyan Bayu; Mengko, Tati L. R.; Gunawan, Hendra

doi:10.1109/lgrs.2010.2046393

Cited by 11 publications

(9 citation statements)

References 6 publications

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“…The visual analysis of these results shows that the proposed approach gives significant filtering improvement with respect to the three other algorithms especially the MRF [41] and the original proposed one [40]. The filtered wrapped phase image φ obtained by the JMRF approach is close to the original one without noise, and this can be confirmed by computing the peak-to-signal noise ratio (PSNR) between the original interferogram without noise and the estimated filtered interferogramφ as given by [50] P SNR(φ, φ) = 10 log 10…”

Section: A Filtering Resultsmentioning

confidence: 63%

See 1 more Smart Citation

A Joint Markov Random Field Approach for SAR Interferogram Filtering and Unwrapping

Abdallah

Abdelfattah

2016

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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This paper presents a new integrated interferometric synthetic aperture radar (InSAR) phase filtering and unwrapping method based on a Markov random field (MRF) model. This approach aims to estimate a noiseless unwrapped phase from the observed noisy interferogram. The phase image is modeled using a joint MRF with a corresponding energy function related simultaneously to noise filtering and phase unwrapping (PU). This function contains two parts: the first is for interferogram filtering process and the second aims to unwrap this filtered phase. The filtered unwrapped phase image is estimated by minimizing the proposed energy function using the genetic algorithm (GA). The proposed approach is tested and validated on simulated and real interferograms acquired with the Radarsat-2, ERS-2, and Envisat satellites acquired over two distinct regions in Tunisia (Mahdia and Ben Guerden) with different geographical characteristics. The obtained results show a significant improvement with respect to other simultaneously filtering and unwrapping algorithms.Index Terms-Genetic algorithm (GA), InSAR phase filtering, Markov random field (MRF), phase unwrapping (PU).

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Section: A Filtering Resultsmentioning

confidence: 63%

“…This can be confirmed by computing the PSNR between the original true phase images, or the DEM of the ASTER satellite, and the filtered-unwrapped ones. The PSNR formula in the case of unwrapping estimation is given by [50] P SNR(Ψ, Ψ) = 10 log 10 Max(Ψ) 2…”

Section: B Unwrapping Resultsmentioning

confidence: 99%

A Joint Markov Random Field Approach for SAR Interferogram Filtering and Unwrapping

Abdallah

Abdelfattah

2016

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…The top left pixel of this raster is designated as the origin (0, 0). The synthetic phase consists of two overlapping Gaussian profiles and is given analytically by While the particular shape of the profile was chosen arbitrarily, Gaussian phase profiles are often used as synthetic phase distributions [ 16 , 44 , 45 ]. Furthermore the Gaussian phase profile cannot simply be fitted by a set of monomial base functions.…”

Section: Methodsmentioning

confidence: 99%

Tile-Based Two-Dimensional Phase Unwrapping for Digital Holography Using a Modular Framework

et al. 2015

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A variety of physical and biomedical imaging techniques, such as digital holography, interferometric synthetic aperture radar (InSAR), or magnetic resonance imaging (MRI) enable measurement of the phase of a physical quantity additionally to its amplitude. However, the phase can commonly only be measured modulo 2π, as a so called wrapped phase map. Phase unwrapping is the process of obtaining the underlying physical phase map from the wrapped phase. Tile-based phase unwrapping algorithms operate by first tessellating the phase map, then unwrapping individual tiles, and finally merging them to a continuous phase map. They can be implemented computationally efficiently and are robust to noise. However, they are prone to failure in the presence of phase residues or erroneous unwraps of single tiles. We tried to overcome these shortcomings by creating novel tile unwrapping and merging algorithms as well as creating a framework that allows to combine them in modular fashion. To increase the robustness of the tile unwrapping step, we implemented a model-based algorithm that makes efficient use of linear algebra to unwrap individual tiles. Furthermore, we adapted an established pixel-based unwrapping algorithm to create a quality guided tile merger. These original algorithms as well as previously existing ones were implemented in a modular phase unwrapping C++ framework. By examining different combinations of unwrapping and merging algorithms we compared our method to existing approaches. We could show that the appropriate choice of unwrapping and merging algorithms can significantly improve the unwrapped result in the presence of phase residues and noise. Beyond that, our modular framework allows for efficient design and test of new tile-based phase unwrapping algorithms. The software developed in this study is freely available.

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“…The data were represented using components such as edges, brightness, and color [13,14]. Digital image consists of a number of rows of bits which represent real and complex values [15,16].…”

Section: Introductionmentioning

confidence: 99%

Role of Deep Learning in Improving the Performance of Driver Fatigue Alert System

Fouad¹,

Labib²

2022

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Driver fatigue detection system aims to monitor the driver state. When detecting a fatigue caused by different attitudes other than normal driving habit, the system warns the driver that traveling should be interrupted. In this way, it helps the driver to make the right decision. The aim of this study is to prevent traffic accidents. The system analyzes any changes in the driver's eyes and mouth features in real time and warns the driver when necessary. The proposed system contains several stages to detect the driver's fatigue. First, the preprocessing stage; enhancement of the frames, determining the face, and cropping eyes and mouth of the driver was done. Then, dealing with feature extraction stage; the features concerning each frame was processed. Finally, two classification approaches were presented and a comparison between them was addressed. In the first approach, four traditional classifiers were applied; Diagonal Linear Discriminant Analysis (DiagLDA), Linear Support Vector Machine (LSVM), K-Nearest Neighbor (KNN), and Random Forest Classifier (RFC). The results show that two classifiers; KNN and RFC yield the highest average accuracy of 91.94% for all subjects presented in this paper. In the second approach, one model of deep learning neural network (CNN) was applied; "Resnet-50" model. The results also show that the proposed deep learning model yields a high average accuracy of 96.3889% for the same data. In general, the drowsiness and lost focus of drivers with high accuracy have been detected with the developed image processing based system, which makes it practicable and reliable for real-time applications.

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Phase Unwrapping by Markov Chain Monte Carlo Energy Minimization

Cited by 11 publications

References 6 publications

A Joint Markov Random Field Approach for SAR Interferogram Filtering and Unwrapping

A Joint Markov Random Field Approach for SAR Interferogram Filtering and Unwrapping

Tile-Based Two-Dimensional Phase Unwrapping for Digital Holography Using a Modular Framework

Role of Deep Learning in Improving the Performance of Driver Fatigue Alert System

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