Image reconstruction from a reduced set of pixels using a simplified gradient algorithm

Stanković, Isidora; Orović, Irena; Stanković, Srdjan

doi:10.1109/telfor.2014.7034455

Cited by 10 publications

(8 citation statements)

References 13 publications

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“…A non-iterative and iterative threshold based solutions for sparse signal reconstruction are proposed in [98]. The proposed solutions are based on the model of noise appearing as a consequence of missing samples.…”

Section: Automated Threshold Based Solutionmentioning

confidence: 99%

“…[87]- [98], biomedical applications [98]- [101], etc. The review of CS applications for different 1D signals, images and video data will be addressed in the sequel.…”

Section: Cs Applicationsmentioning

confidence: 99%

“…(B) Lustig et al in [98] implemented CS approach for rapid MRI imaging. Sparsity of the MRI in the transform domain is exploited for achieving two goals: reducing the scan time and improving the resolution of the observed fast spin-echo brain images and 3D contrast enhanced angiographs.…”

Section: Cs In Biomedical Applicationsmentioning

confidence: 99%

“…frequency space) data from the scanner and χ χ χ χ is a regularization parameter. The conjugate gradient procedure is described in detail in [98]. Restoration [101].…”

Section: Cs In Biomedical Applicationsmentioning

confidence: 99%

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On some common compressive sensing recovery algorithms and applications

Draganic

Orović

Stanković

2017

Facta Univ Electron Energ

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Compressive Sensing, as an emerging technique in signal processing is reviewed in this paper together with its' common applications. As an alternative to the traditional signal sampling, Compressive Sensing allows a new acquisition strategy with significantly reduced number of samples needed for accurate signal reconstruction. The basic ideas and motivation behind this approach are provided in the theoretical part of the paper. The commonly used algorithms for missing data reconstruction are presented. The Compressive Sensing applications have gained significant attention leading to an intensive growth of signal processing possibilities. Hence, some of the existing practical applications assuming different types of signals in real-world scenarios are described and analyzed as well.

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Section: Automated Threshold Based Solutionmentioning

confidence: 99%

“…[87]- [98], biomedical applications [98]- [101], etc. The review of CS applications for different 1D signals, images and video data will be addressed in the sequel.…”

Section: Cs Applicationsmentioning

confidence: 99%

Section: Cs In Biomedical Applicationsmentioning

confidence: 99%

“…frequency space) data from the scanner and χ χ χ χ is a regularization parameter. The conjugate gradient procedure is described in detail in [98]. Restoration [101].…”

Section: Cs In Biomedical Applicationsmentioning

confidence: 99%

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On some common compressive sensing recovery algorithms and applications

Draganic

Orović

Stanković

2017

Facta Univ Electron Energ

Self Cite

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“…In that sense, we propose using a popular compressive sensing (CS) approach in order to deal with randomly undersampled terrain photos, obtained as a result of smart sensing strategy or removal of impulse noise. The CS reconstruction algorithms may deal with images having large amount of missing or corrupted pixels [10][11][12][13][14][15][16]. The missing pixels can be subject of the reduced sampling strategy when a certain amount of pixels at random positions are omitted from observations or may appear as a consequence of discarding pixels affected by certain errors or noise as discussed in the sequel [14].…”

Section: Introductionmentioning

confidence: 99%

Gradient Compressive Sensing for Image Data Reduction in UAV Based Search and Rescue in the Wild

Musić

Orović

Marasović

et al. 2016

Mathematical Problems in Engineering

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Search and rescue operations usually require significant resources, personnel, equipment, and time. In order to optimize the resources and expenses and to increase the efficiency of operations, the use of unmanned aerial vehicles (UAVs) and aerial photography is considered for fast reconnaissance of large and unreachable terrains. The images are then transmitted to control center for automatic processing and pattern recognition. Furthermore, due to the limited transmission capacities and significant battery consumption for recording high resolution images, in this paper we consider the use of smart acquisition strategy with decreased amount of image pixels following the compressive sensing paradigm. The images are completely reconstructed in the control center prior to the application of image processing for suspicious objects detection. The efficiency of this combined approach depends on the amount of acquired data and also on the complexity of the scenery observed. The proposed approach is tested on various high resolution aerial images, while the achieved results are analyzed using different quality metrics and validation tests. Additionally, a user study is performed on the original images to provide the baseline object detection performance.

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