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Speckle filtering by generalized gamma distribution
(2009-12-01)
Intajag, Sathit
;
Sukkasem, Nopparat
Synthetic aperture radar (SAR) image contains undesired artifacts in the form of a granular look, which is called speckle. Methods previously proposed for speckle noise filtering suffer from two major limitations: 1) noise attenuation is not sufficient, especially in the smooth and background areas; 2) existing methods do not preserve or enhance edges sufficiently-they only inhibit smoothing near edges. The proposed method is capable of reducing the speckle more effectively than previous methods and jointly enhancing the edge information, rather than just inhibiting smoothing. Our method employs the generalized gamma distribution (GGD) to model the SAR images and adopts maximum-likelihood method to estimate GGD's parameters, which use for constructing the speckle filter. Finally, the proposed method is evaluated and compared to well-accepted methods through real data. © 2009 IEEE.
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Retinal image enhancement in multi-mode histogram
(2009-11-16)
Intajag, Sathit
;
Tipsuwanporn, Vittaya
;
Chatthai, Rungrat
Evaluation of a retinal image is widely employed to help doctors diagnose many diseases, such as diabetes or hypertension. From acquisition process, retinal images often have low grey level contrast and dynamic range. This paper proposes histogram analysis for solving the problems of retinal image enhancement. The proposed method uses fuzzy set to enhance the images by partitioning the image histogram to multi-modes with derivative equation. Each mode of the histogram is contrasted by finding the optimal crossover point of S-function with an index of fuzziness, which is designed for contrasting a field of view in the images. Our algorithm can achieve a number of properties of an importance for contrast stretching, such as compressed the noise in background and produced a high contrast in the field of view that provide feasibility in diagnosis from a retinal image. © 2008 IEEE.
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Retinal image enhancement by indices of fuzziness
(2008-12-01)
Intajag, Sathit
;
Laohasongkram, Phiphat
;
Chatree, Pongtorn
Evaluation of a retinal image is widely employed to help doctors diagnose many diseases, such as diabetes or hypertension. From acquisition process, retinal images often have low grey level contrast and dynamic range. This paper proposes indices of fuzziness for solving the problems of retinal image enhancement. Histogram of the retinal image is stretched by estimating the optimal parameters of the indices of fuzziness in S-function, which is designed for contrasting a field of view in the images. Our algorithm can achieve a number of significant properties for contrast stretching, such as compressed the noise in background and produced a high contrast in a field of view or foreground that provides feasibility in diagnosis from a retinal image. © 2008 SICE.
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Retinal image enhancement by index of fuzziness in multi-mode histogram
(2008-12-01)
Intajag, Sathit
;
Chatthai, Rungrat
;
Tipsuwanporn, Vittaya
Evaluation of a retinal image is widely employed to help doctors diagnose many diseases, such as diabetes or hypertension. From acquisition process, retinal images often have low grey level contrast and dynamic range. This paper proposes histogram analysis for solving the problems of retinal image enhancement. The proposed method uses fuzzy set to enhance the images by partitioning the image histogram to multi-modes with derivative equation. Each mode of the histogram is contrasted by finding the optimal crossover point of S-function with an index of fuzziness, which is designed for contrasting a field of view in the images. Our algorithm can achieve a number of properties of an importance for contrast stretching, such as compressed the noise in background and produced a high contrast in the field of view that provide feasibility in diagnosis from a retinal image.
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SAR segmentation using anisotropic diffusion
(2008-01-01)
Intajag, Sathit
;
Tipsuwanporn, Vittaya
;
Cheevasuwit, Fusak
Speckle effects are commonly observed in synthetic aperture radar (SAR) images. The human eye is capable of deriving meaningful information from SAR images; however, an automatic or semi-automatic processing algorithm has difficulty in distinguishing objects in the images because of noise effects present in those images. This paper presents a segmentation method for SAR images, which employs an anisotropic diffusion algorithm. In the proposed scheme, a SAR image is transformed into a logarithmic domain where the diffusion process is used to grow homogeneous regions in the noise environment until the regions reach some criteria for homogeneity; consequently, the segmented image in the logarithm domain is converted to the intensity domain by an exponential function. To grow homogeneous regions the adaptive diffusion method is introduced with a tensor technique in which tensor data are varied with the neighboring pixels. The diffusion algorithm will stop itself by a standard deviation divided by the mean, which is provided according to the homogeneity criteria. Results are shown on both synthetic and satellite SAR images. The evaluation of the proposed method employs the theoretical gain of equivalent numbers of looks (ENL). © 2008 The Institute of Electrical Engineers of Japan.
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Speckle noise estimation with generalized gamma distribution
(2006-12-01)
Intajag, Sathit
;
Chitwong, Sakreya
Speckle noise is an inherent property of a synthetic aperture radar (SAR) image, and it generally tends to reduce the image resolution and contrast. The speckle noise estimation is an important prerequisite, whenever SAR image is used for object segmentation. Among the many methods in statistical description that have been proposed to perform the estimation, there exists a class of approaches that use a multiplicative model of speckled image formation, such as Rayleigh distribution, K-distribution, Weibull distribution etc. In this paper, generalized gamma (GG) distribution is used to estimate the noise characteristics. GG distribution is especially attractive because it contains several distributions as special cases, viz. Rayleigh, exponential, Weibull, and log-normal. The major parameter of the GG distribution is estimated according to maximum likelihood (ML) principle. The proposed method works successfully when the solution is located in the parameter space. For verifying the performance of the proposed scheme compared to the other methods, we use a χ<sup>2</sup> goodness-of-fit (GOF) test. © 2006 ICASE.
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SAR segmentation using anisotropic diffusion
(2005-12-01)
Intajag, Sathit
;
Tipsuwanpon, Vittaya
Speckle effects are commonly observed in a synthetic aperture radar (SAR) image, which applies in many land applications, such as agriculture or forestry. However, although the human eye is able to derive the meaningful information from the SAR image, automatic processing is very difficult to distinguish the objects in the images because of the noise effect present in the image. This paper presents a segmentation method of the SAR images, which uses an anisotropic diffusion algorithm with an inherence region-growing algorithm. Anisotropic diffusion is modified in logarithmic domain to remove the speckle noise and to identify the homogeneous regions in ERS and JERS satellite data. The modified diffusion method is formulated with a tensor technique, varying with both the location of a pixel in the image and the orientation of the local image geometry vicinity the pixels. In our algorithm, SAR images are transformed into logarithmic domain and using the modified anisotropic diffusion to grow the homogeneous regions in the noise environment; consequently, the segmented image in the logarithm domain is converted to the intensity domain by an exponential function. On the other hand, our diffusion algorithm can stop itself by a standard deviation to mean criteria. Simulation results on the satellite images are provided to indicate the effectiveness of the proposed method when used to remove the effects of speckle noise almost as well as that of the additive noise. The evaluation of the proposed method employs the theoretical gain in equivalent numbers of looks (ENL). © 2005 SICE.
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Anisotropic diffusion in synthetic aperture radars
(2005-12-01)
Intajag, Sathit
;
Tipsuwanpon, Vittaya
;
Cheevasuwit, Fusak
Speckle effects are commonly observed in synthetic aperture radar (SAR) image, which applies in many land applications, such as agriculture or forestry. However, although the human eye is able to derive the meaningful information from the SAR image, automatic processing is very difficult because of the noise effect present in the image. In this paper, anisotropic diffusion is modified in logarithmic domain to remove the speckle noise in ERS and JERS satellite data. The modified diffusion method is employed as a tensor, varying with both the location of a pixel in the image and the orientation of the local image geometry vicinity the pixel. In our algorithm, SAR images are transformed into logarithmic domain and using the modified anisotropic diffusion to smooth out the noise; consequently, the smoothed image is converted by an exponential function. Simulation results on the satellite images are provided to indicate the effectiveness of the proposed method when used to remove the effects of speckle noise almost as well as that of the additive noise. The evaluation of the proposed method employs the theoretical gain in equivalent numbers of looks (ENL). © 2005 IEEE.
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Speckle reduction using fuzzy morphological anisotropic diffusion
(2005-12-01)
Easanuruk, Somchai
;
Mitatha, Somsak
;
Intajag, Sathit
;
Chitwong, Sakreya
One of important tasks of radar image processing is reducing speckle noise as preprocessing to enhance preformance of other processing such as segmentation, classification, etc. In this paper, we then apply the fuzzy morphology together with anisotropic diffusion to reduce speckled noise of SAR image. Anisotropic diffusion is designed based on additive noise model, but the form of speckled image is in multiplicative speckle model. To transform additive noise model into multiplicative speckle model, logarithmic transformation is then used. Our algorithm performs in log-domain. Finally, despeckled image being in log-domain is converted into spatial domain by using exponential transformation. Simulated image as speckled image is performed with our algorithm to show and compare results with recent reports in term of both signal to noise ratio (SNR) and the equivalent number of looks (ENL). Also, real SAR image is performed to confirm results in term of ENL only. Results from our experiment are shown that de-speckled image can smooth out in homogeneous area and preserve edge in heterogeneous area. Both visual image and numerical results are used to show all results. © 2005 IEEE.
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Automatic SAR segmentation by fuzzy hit-or-miss and homogeneity index
(2005-12-01)
Intajag, Sathit
;
Tipsuwanporn, Vittaya
;
Chitwong, Sakreya
;
Suesut, Taweepon
;
Chevasuwit, Fusak
Object-based segmentation is the first essential step for image processing applications. Recently, segmentation techniques have been developed, but not enough to preserve the significant information contained in the small regions of an image. The proposed method is to partition an image into homogeneous regions by using a fuzzy hitor- miss operator with an inherent spatial transformation, which endows to preserve the small regions. In the proposed scheme, an iterative segmentation technique is formulated as consequential processes. Then, each time in iterating, hypothesis testing is used to evaluate the quality of the segmented regions with a homogeneity index. Our method is unsupervised and uses few parameters, most of which can be calculated from input data. This comparative study indicates that the new iterative segmentation algorithm provides acceptable results as seen in the tested examples of synthetic aperture radar (SAR) images. © 2005 by the American Society for Photogrammetry and Remote Sensing.

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