SAR Image Denoising Using the Non-Subsampled Contourlet Transform and Morphological Operators

Muñoz, José Manuel Mejía; Domínguez, Humberto de Jesús Ochoa; Maynez, Leticia Ortega; Villegas, Osslan Osiris Vergara; Sánchez, Vianey Guadalupe Cruz; Gordillo, Nelly; Casas, Efrén David Gutiérrez

doi:10.1007/978-3-642-16761-4_30

Cited by 5 publications

(6 citation statements)

References 6 publications

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“…In this work, NSP and NSDFB of (4) and (5) are implemented using ‘maxflat’ and ‘dmaxflat7’ filters. Maxflat is for pyramidal decompositions and dmaxflat of order 7 is for directional subbands per scale [15].…”

Section: Proposed Methodsmentioning

confidence: 99%

Non‐subsampled contourlet transform based image Denoising in ultrasound thyroid images using adaptive binary morphological operations

Babu

Sudha

2014

IET Computer Vision

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Speckle noise reduction is an important preprocessing stage for ultrasound medical image processing. In this paper, a despeckling algorithm is proposed based on non-subsampled contourlet transform. This transform has the property of high directionality, anisotropy and translation invariance, which can be controlled by non-subsampled filter banks. This study aims to denoise the speckle noise in ultrasound images using adaptive binary morphological operations, in order to preserve edges, contours and textures. In morphological operations, structural element plays an important role for image enhancement. In this work, different shapes of structural element have been analysed and filtering parameters have been changed adaptively depending on the nature of the image and the amount of noise in the image. Experimental results of proposed method for natural images, Field II simulated images and real ultrasound images, show that the proposed method is able to preserve edges and image structural details compared with existing methods.

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Section: Proposed Methodsmentioning

confidence: 99%

Non‐subsampled contourlet transform based image Denoising in ultrasound thyroid images using adaptive binary morphological operations

Babu

Sudha

2014

IET Computer Vision

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“…Wavelet transform has good localization characteristics and multiresolution analysis capabilities in time-frequency domain, which has resulted in its extensive application prospect in image denoising areas (Celebi et al, 2009). Many new transform methods, such as Contourlet (Mejła Mu oz et al, 2010) and Curvelet (Guanying et al, 2011) transform, have advantages in directional selectivity by virtue of their multiscale and anisotropic properties. So theoretically speaking, these methods can filter speckle and stripe noise in SSS images more effectively.…”

Section: Related Workmentioning

confidence: 99%

Edge preserving filter of side scan sonar images with wavelet modulus maxima shift‐correlative technique

Tao

Liu

2011

Int J Imaging Syst Tech

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An edge preserving filter algorithm of side scan sonar (SSS) image based on wavelet modulus maxima shift-correlative (WMMS) technique is proposed in this article. First, the proposed WMMS algorithm decomposes SSS image into multiscale wavelet coefficients. Then the modulus maxima, which are produced by catastrophe points, are extracted from wavelet coefficients. The algorithm matches these maxima across the different scales to identify signal or noise. After correcting the ''drifting'' phenomenon of modulus maxima, a correlation factor array of wavelet coefficients is constructed by strengthening the maxima dominated by signal and suppressing those maxima dominated by noise. By correlating wavelet coefficients with the correlation factor array, the WMMS strengthens the useful high-frequency signal and weakens the noise. Finally, the algorithm restores SSS image from revised wavelet coefficients. We apply the WMMS algorithm to filter SSS images of the experimental sea areas. Results show that WMMS has advantages over traditional algorithms in suppressing noise and preserving useful high-frequency information.

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“…In an ultrasound picture, speckle noise displays a granular texture, and it is due to constructive and destructive ultrasound waves with objects from sub-resolution scatters [31]. The presence of speckle noise can explain using statistical and probabilistic approaches.…”

Section: Modeling Of Speckle Noisementioning

confidence: 99%

“…Then, the 'max-flat' and 'dmaxflat7' filters implement NSP and NSDFB [31]. Assume G 0 , G 1 and V equ k be the reconstruction filters of H 0 , H 1 and U equ k respectively, for reconstruction.…”

Section: Modeling Of Speckle Noisementioning

confidence: 99%

Adaptive Fuzzy Logic Despeckling in Non-Subsampled Contourlet Transformed Ultrasound Pictures

Manikandan¹,

Karthikeyan²,

Babu³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Signal to noise ratio in ultrasound medical images captured through the digital camera is poorer, resulting in an inaccurate diagnosis. As a result, it needs an efficient despeckling method for ultrasound images in clinical practice and telemedicine. This article proposes a novel adaptive fuzzy filter based on the directionality and translation invariant property of the Non-Sub sampled Contour-let Transform (NSCT). Since speckle-noise causes fuzziness in ultrasound images, fuzzy logic may be a straightforward technique to derive the output from the noisy images. This filtering method comprises detection and filtering stages. First, image regions classify at the detection stage by applying fuzzy inference to the directional difference obtained from the NSCT noisy image. Then, the system adaptively selects the better-suited filter for the specific image region, resulting in significant speckle noise suppression and retention of detailed features. The suggested approach uses a weighted average filter to distinguish between noise and edges at the filtering stage. In addition, we apply a structural similarity measure as a tuning parameter depending on the kind of noise in the ultrasound pictures. The proposed methodology shows that the proposed fuzzy adaptive filter effectively suppresses speckle noise while preserving edges and image detailed structures compared to existing approaches.

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SAR Image Denoising Using the Non-Subsampled Contourlet Transform and Morphological Operators

Cited by 5 publications

References 6 publications

Non‐subsampled contourlet transform based image Denoising in ultrasound thyroid images using adaptive binary morphological operations

Non‐subsampled contourlet transform based image Denoising in ultrasound thyroid images using adaptive binary morphological operations

Edge preserving filter of side scan sonar images with wavelet modulus maxima shift‐correlative technique

Adaptive Fuzzy Logic Despeckling in Non-Subsampled Contourlet Transformed Ultrasound Pictures

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