Sonar target enhancement by shrinkage of incoherent wavelet coefficients

Hunter, Alan J.; Vossen, R. van

doi:10.1121/1.4845255

Cited by 21 publications

(18 citation statements)

References 12 publications

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“…2. The size of the blocks in the grid is chosen so that information of the point scatterers is preserved over several blocks, while the speckle background is correlated on a much smaller scale than the block size [1]. The point scatterers will then be persistent in both of the resulting two complimentary looks, whereas the speckle will ideally not have high coherence between the looks.…”

Section: A Phantom Data Setsmentioning

confidence: 99%

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Point scatterer enhancement in ultrasound by wavelet coefficient shrinkage

Hverven¹,

Rindal²,

Hunter³

et al. 2017

2017 IEEE International Ultrasonics Symposium (IUS)

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Background speckle can often obscure objects of interest in an ultrasound image. The probability of detection and classification of point scatterers is highly affected by background speckle. The proposed algorithm uses a coherence-based wavelet coefficient shrinkage method. Point scatterers in the ultrasound image are enhanced by separating coherent point targets from incoherent background speckle. Results using Field II ultrasound simulations show how the algorithm retains the point scatterers and increases their conspicuity. The algorithm has potential to detect microcalcifications in breast tissue.

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Section: A Phantom Data Setsmentioning

confidence: 99%

“…The wavelet coefficients with low coherence are attenuated to produce an image with reduced speckle. The method has previously been applied on sonar images to separate coherent targets from incoherent background reverberation noise [1].…”

Section: Introductionmentioning

confidence: 99%

Point scatterer enhancement in ultrasound by wavelet coefficient shrinkage

Hverven¹,

Rindal²,

Hunter³

et al. 2017

2017 IEEE International Ultrasonics Symposium (IUS)

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“…In order to separate the targets from the background, a coherence metric can be used. In [10], a coherence metric was derived to determine the similarity of wavelet coefficients between independent looks, i.e. different images of the same scene with statistically independent noise realizations.…”

Section: Target / Background Separation By Incoherent Wavelet Shrinkagementioning

confidence: 99%

“…By weighting the image according to this coherence, a separation can be made between targets and background. A thorough description of this method can be found in [10].…”

Section: Target / Background Separation By Incoherent Wavelet Shrinkagementioning

confidence: 99%

UXO Detector for Underwater Surveys Using Low-Frequency Sonar

Vossen¹,

Hunter²,

Duijster³

et al. 2015

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“…Acoustic remote sensing technologies, such as high-resolution multibeam and side-scan sonars imaging in water, are widely used in marine geology, commercial fishing, offshore oil prospecting and drilling, and so forth [1][2][3][4]. Due to transmission loss and acoustic wave scattering, sonar images are notorious for low contrast, edge-blurring, and being full of noise.…”

Section: Introductionmentioning

confidence: 99%

Automatic Side-Scan Sonar Image Enhancement in Curvelet Transform Domain

Zhou

Huo

2015

Mathematical Problems in Engineering

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We propose a novel automatic side-scan sonar image enhancement algorithm based on curvelet transform. The proposed algorithm uses the curvelet transform to construct a multichannel enhancement structure based on human visual system (HVS) and adopts a new adaptive nonlinear mapping scheme to modify the curvelet transform coefficients in each channel independently and automatically. Firstly, the noisy and low-contrast sonar image is decomposed into a low frequency channel and a series of high frequency channels by using curvelet transform. Secondly, a new nonlinear mapping scheme, which coincides with the logarithmic nonlinear enhancement characteristic of the HVS perception, is designed without any parameter tuning to adjust the curvelet transform coefficients in each channel. Finally, the enhanced image can be reconstructed with the modified coefficients via inverse curvelet transform. The enhancement is achieved by amplifying subtle features, improving contrast, and eliminating noise simultaneously. Experiment results show that the proposed algorithm produces better enhanced results than state-of-the-art algorithms.

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Sonar target enhancement by shrinkage of incoherent wavelet coefficients

Cited by 21 publications

References 12 publications

Point scatterer enhancement in ultrasound by wavelet coefficient shrinkage

Point scatterer enhancement in ultrasound by wavelet coefficient shrinkage

UXO Detector for Underwater Surveys Using Low-Frequency Sonar

Automatic Side-Scan Sonar Image Enhancement in Curvelet Transform Domain

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