Bionic vision-based synthetic aperture radar image edge detection method in non-subsampled contourlet transform domain

Huo, Guanying; Li, H.; Ma, Gehua; Shi, Aiye

doi:10.1049/iet-rsn.2011.0193

Cited by 13 publications

(8 citation statements)

References 27 publications

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“…In this research, edge extractions were performed on real and simulated SAR images, which showed that proposed methods is better than other edge extraction methods (Li, Q. et al, 2012). Retinal image analysis using Curvelet Transform and multistructure elements morphology by reconstruction.…”

Section: Introductionmentioning

confidence: 92%

Reconstruction performances of curvelet transform for magnetic resonance images

Hussain

Memon

2014

J Engin Res

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Section: Introductionmentioning

confidence: 92%

Reconstruction performances of curvelet transform for magnetic resonance images

Hussain

Memon

2014

J Engin Res

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“…These detectors are simple to implement, however, because of their dependence on window size being selected, they are not immune to noise. Though multiscale analysis tools have been developed for edge detection which could counter many of these issues [4]- [7]. But when it comes to fully polarimetric SAR images, the research efforts on this front have found to be lacking.…”

Section: Introductionmentioning

confidence: 99%

“…Further transform based techniques have became popular. A two-scale line detection method was proposed in [7] using curvelet transform. Nonsubsampled contourlet transform (NSCT) based multiscale edge detection method was proposed in [10], [11] and object detection based on bandelet transform dealt in [12] where bandelet applied on the spatial domain.…”

Section: Introductionmentioning

confidence: 99%

Edge Detection in Polarimetric SAR Image based on Bandelet Transform

Thankachan¹,

Sethunadh²,

Ameer³

2019

IJCA

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Polarimetric Synthetic Aperture Radar is being used widely in order to extract the features of target image including civilian and military applications. Edge detection in PolSAR images is highly challenging task due to the occurrence of speckle noise in these images although widely used for the strategic applications. This paper presents a multi-resolution edge detection method for PolSAR images using the bandelet transform. Bandelet transform can provide flexible multiscale and directional decomposition of images including SAR images. Edge enhancement of the input image is done after decomposing it using bandelet transform and the resultant bandelet coefficients are modified through the maximisation of the polarimetric contrast between the adjacent subbands using Lagrangian methods. Taking advantage of the directional features of bandelet transform an algorithm is developed for retaining the geometrical features of images such as edges, boundaries etc present in SAR images while ensuring effective speckle noise removal. Here the geometrical features in images are enhanced in the bandelet domain by fusing the different directional subband coefficients at different scales. The performance of this method is verified using real PolSAR images. The result shows that proposed scheme eliminates speckle noise and retrieved edges are continuous and complete.

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“…Then J. P. Zhou et al proposed nonsubsampled contourlet transform (NSCT) which is a shift-invariant version of CT [9,10]. Due to the properties of directionality, anisotropy and shift-invariance, NSCT has been widely applied in image enhancement, segmentation, edge detection and so on [11][12][13][14]. In [15], H. Soyel et al proposed a NSCT-based automatic image enhancement method which enhances the contrast by modifying the NSCT coefficients with a nonlinear mapping function.…”

Section: Introductionmentioning

confidence: 99%

Multi-party Password-Authenticated Key Exchange Scheme with Privacy Preservation for Mobile Environment

2016

KSII TIIS

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Underwater image enhancement has received considerable attention in last decades, due to the nature of poor visibility and low contrast of underwater images. In this paper, we propose a new automatic underwater image enhancement algorithm, which combines nonsubsampled contourlet transform (NSCT) domain enhancement techniques with the mechanism of the human visual system (HVS). We apply the multiscale retinex algorithm based on the HVS into NSCT domain in order to eliminate the non-uniform illumination, and adopt the threshold denoising technique to suppress underwater noise. Our proposed algorithm incorporates the luminance masking and contrast masking characteristics of the HVS into NSCT domain to yield the new HVS-based NSCT. Moreover, we define two nonlinear mapping functions. The first one is used to manipulate the HVS-based NSCT contrast coefficients to enhance the edges. The second one is a gain function which modifies the lowpass subband coefficients to adjust the global dynamic range. As a result, our algorithm can achieve contrast enhancement, image denoising and edge sharpening automatically and simultaneously. Experimental results illustrate that our proposed algorithm has better enhancement performance than state-of-the-art algorithms both in subjective evaluation and quantitative assessment. In addition, our algorithm can automatically achieve underwater image enhancement without any parameter tuning.

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Bionic vision-based synthetic aperture radar image edge detection method in non-subsampled contourlet transform domain

Cited by 13 publications

References 27 publications

Reconstruction performances of curvelet transform for magnetic resonance images

Reconstruction performances of curvelet transform for magnetic resonance images

Edge Detection in Polarimetric SAR Image based on Bandelet Transform

Multi-party Password-Authenticated Key Exchange Scheme with Privacy Preservation for Mobile Environment

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