SAR image compression using wavelet transform and soft-thresholding

Li, Y.; Moloney, Cecilia

doi:10.1109/ccece.1997.614870

Cited by 8 publications

(5 citation statements)

References 5 publications

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“…A critical step in the image compression process is the proper selection of a threshold to reduce the number of the wavelet coefficients. Several studies have addressed the issue of threshold selection that ranged from experimental trial and error [7][8][9] to using models of the Human Visual System (HVS) to take advantage of the spatial clustering of pixels [10][11].…”

Section: H Image Compressionmentioning

confidence: 99%

Image Data Compression by Adaptive Thresholding of Wavelet Coefficients

Abdallah¹,

Singh²

1999

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Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing this collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, SUPPLEMENTARY NOTES ABSTRACTImage compression is performed to reduce the bit rate for image transmission and the required storage in the case of image archival. Typically, it involves a transformation of the image to reduce the correlation and redundancy between pixels. The wavelet transform has gained wide acceptance as a tool to decorrelate most image sources. The transformed image is decomposed at different scales of details and approximation. Such decomposition facilitates image compression and progressive transmission. In this study, a system and method for image compression is presented based on the wavelet transformation. A threshold is determined adaptively based on the desired compression ratio and only significant coefficients are retained. . SUBJECT TERMS . Image Data Compression by Adaptive Thresholding of Wavelet CoefficientsMahmoud A. Abdallah Ram-Nandan P. Singh ABSTRACTImage compression is performed to reduce the bit rate for image transmission and the required storage m the case of image archival. Typically, it involves a transformation of the image to reduce the correlation and redundancy between pixels. The wavelet transform has gained wide acceptance as a tool to decorrelate most image sources. The transformed image is decomposed at different scales of details and approximation. Such decomposition facilitates image compression and progressive transmission. In this study, a system and method for image compression is presented based on the wavelet transformation. A threshold is determined adaptively based on the desired compression ratio and only significant coefficients are retained.

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Section: H Image Compressionmentioning

confidence: 99%

Image Data Compression by Adaptive Thresholding of Wavelet Coefficients

Abdallah¹,

Singh²

1999

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“…More recently, there has been active research on denoising with the wavelet transform; Donoho and Johnstone [7] were the first to propose a novel approach for noise reduction by employing threshold in the wavelet domain. Various wavelet bases and threshold techniques have been used in reduction of speckle noise in SAR images [8,9]. Recently several works [10,11,12,13] have been conducted to improve the quality of anisotropic diffusion in speckle reduction.…”

Section: Introductionmentioning

confidence: 99%

“…Recently several works [10,11,12,13] have been conducted to improve the quality of anisotropic diffusion in speckle reduction. The most representative one among them is SRAD method proposed by Yu and Acton [8]. So in this paper, we outline a partial differential equation (PDE) approach to anisotropic diffusion (AD), and speckle removal that we call speckle reducing anisotropic diffusion (SRAD).…”

Section: Introductionmentioning

confidence: 99%

A new hybrid algorithm for speckle noise reduction of SAR images based on mean-median filter and SRAD method

Rahimi

Yazdi

2015

2015 2nd International Conference on Pattern Recognition and Image Analysis (IPRIA)

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One of the inherent characteristics of radar images is the presence of speckle noise. Speckle appears as a grainy texture in the image and highly reduces the image quality. Therefore, it is desirable to reduce speckle, prior to any image interpretation. With regard to the importance of synthetic aperture radar (SAR) images, a lot of efforts have already been made to remove speckle noise from radar images, and accordingly famous filters have been introduced, each with their special advantages and disadvantages. In this paper, we examine five methods like the ones in the field of space and frequency domain. we will compare five different approaches: Wavelet Thresholding methods, anisotropic diffusion and speckle reducing anisotropic diffusion, also we suggest a method for reducing speckle of synthetic aperture radar images which is in fact a combination of hybrid mean-median filter and the method of speckle reducing anisotropic diffusion. The results indicate that the performance of our proposed method, based on criteria such as PSNR, improving SNR, standard protect the edge ( ), in almost all cases is better the other compared methods; and it also offers more desirable results from the point of visual quality.

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“…In both cases, however, the noise is assumed to be white and independent of the noise-free imaged signal. In recent years, various wavelet bases and thresholding techniques have been used in reduction of speckle noise in SAR image [4][5][6]. As images have the multidirectional and anisotropy characteristics, and the twodimensional wavelet transform can not make use of their image geometric regularity, second-generation Bandelets [7][8] provide a new image representation based on the edge, which can be adaptive to track image geometric regularity.…”

Section: Introductionmentioning

confidence: 99%

Despeckling SAR Images Using Bayes-Shrink in Bandelet Domain

Gao

et al. 2009

2009 2nd International Congress on Image and Signal Processing

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Synthetic aperture radar (SAR) images are inherently affected by multiplicative speckle noise, which is due to the coherent nature of scattering phenomena. This paper presents a despeckling method for SAR images based on adaptive bandelets thresholding. This threshold is derived in a Bayesian framework. The proposed threshold is simple and closed form, and it is applied to adaptive bandelets coefficients to achieve more satisfying results. The performances of adaptive bandelets Bayesshrink soft-thresholding and wavelet thresholding for despeckling SAR images are compared through an experiment.Experiment results clearly demonstrated the capability of the proposed scheme in SAR image speckle reduction especially for SAR images possessing detailed textures.

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SAR image compression using wavelet transform and soft-thresholding

Cited by 8 publications

References 5 publications

Image Data Compression by Adaptive Thresholding of Wavelet Coefficients

Image Data Compression by Adaptive Thresholding of Wavelet Coefficients

A new hybrid algorithm for speckle noise reduction of SAR images based on mean-median filter and SRAD method

Despeckling SAR Images Using Bayes-Shrink in Bandelet Domain

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