Speckle Suppression And Analysis For Synthetic Aperture Radar Images

Lee, Jong-Sen

doi:10.1117/12.7973877

Cited by 440 publications

(151 citation statements)

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“…A comparison is made between this 2-D ABKF and the local linear minimum variance estimator (LLMVE) in [13], [14], and [18].…”

Section: Implementatio N and Resultsmentioning

confidence: 99%

“…Combining the image generation model in (12) and the observation model in (14) would yield a complete block dynamic model with uncertain observation [26], [27] due to the presence of the multiplicative noise. This 2-D block state-space dynamic model is (15) to be BIBO stable, it is necessary that all the eigenvalues of matrices A o .…”

Section: Equationsmentioning

confidence: 99%

“…Another approach, which is based upon continuous adaptation of filter parameters during the scanning process in a local window, has been proposed by Lee [13]. Later, he used this approach for speckle noise removal using Sigma filters [14]. In this paper a new method for speckle noise reduction is proposed.…”

Section: Introductionmentioning

confidence: 99%

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Two-dimensional adaptive block Kalman filtering of SAR imagery

Azimi-Sadjadi

Bannour

1991

IEEE Trans. Geosci. Remote Sensing

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Abstract-Speckle effects are commonly observed in synthetic aperture radar (SAR) imagery. In airborne SAR systems the effect of this degradation reduces the accuracy of detection substantially. Thus, the elimination of this noise is an important task in SAR imaging systems. In this paper a new method for speckle noise removal is mtroduced using 2-D adaptive block Kalman filtering (ABKF). The image process is represented by an autoregressive (AR) model with nonsymmetric half-plane (NSHP) region of support. New 2-D Kalman filtering equations are derived which take into account not only the effect of speckles as a multiplicative noise but also those of the additive receiver thermal noise and the blur. This method assumes local stationarity within a processing window, whereas the image can be assumed to be globally nonstationary. A recursive identification process using the stochastic Newton approach is also proposed which can be used on-line to estimate the filter parameters based upon the information within each new block of the image. Simulation results on several images are provided to indicate the effectiveness of the proposed method when used to remove the effects of speckle noise as well as that of the additive noise.

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“…A comparison is made between this 2-D ABKF and the local linear minimum variance estimator (LLMVE) in [13], [14], and [18].…”

Section: Implementatio N and Resultsmentioning

confidence: 99%

Section: Equationsmentioning

confidence: 99%

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Two-dimensional adaptive block Kalman filtering of SAR imagery

Azimi-Sadjadi

Bannour

1991

IEEE Trans. Geosci. Remote Sensing

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“…Local statistic filter based on MMSE was proposed by Lee [16]. The original multiplicative signal model to the sum of signal and adaptive noise and derived the local statistics of original image from the noisy one.…”

Section: H Mean Square Error Filtermentioning

confidence: 99%

A Review on Analysis of Speckle reduction in Ultra Sound Images

Bhat¹

2018

IJRASET

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Ultrasound is an inexpensive and widely used imaging modality for the diagnosis and staging of a number of diseases. In the past two decades, it has benefited from major advances in technology and has become an indispensable imaging modality, due to its flexibility and non-invasive character. Ultrasound imaging utilizes high-frequency sound waves to picture inner structures by contrasting reflection signals created when a light emission is anticipated into the body and ricochets back at interfaces between the structures. However the convenience of ultrasound imaging is corrupted by the nearness of flag dependant noise known as dot (speckle). The speckle pattern depends on the structure of the image tissue and various imaging parameters. Speckle noise show in ultrasound picture influences edges and fine subtle elements which constrain the differentiation determination and make symptomatic more troublesome. There are two main purposes for speckle reduction in medical ultrasound imaging (i) To improve the human interpretation of ultrasound images (ii) De-speckling is the preprocessing step for many ultrasound image processing tasks such as segmentation and registration. Various strategies have been proposed for decrease speckle noise in ultrasound imaging. The objective of this paper is to give an overview of speckle noise reduction in ultrasound imaging. Keywords: Speckle noise, Ultrasound image, Different methods, filters analysis I. INTRODUCTIONUltrasound imaging assumes pivotal part in therapeutic imaging because of its non-obtrusive nature, ease and capacity of framing ongoing imaging. Medical Ultrasound imaging is done using ultrasonic waves in 3 to 20 MHz range. Ultrasound waves are created from the transducer and go through body tissues and when the wave achieves a protest or surface with various surface or acoustic in nature, it is reflected back. These echoes are received by the apparatus (the transducer array) and converted into electric current. These signals are amplified, conditioned and shown on a display device in real time. The image generated using Ultrasound imaging commonly known as Ultrasound Scanning is called an Ultra sonogram. The resolution of the image will be better by using higher frequencies but at the same time limits the depth of the penetration. There are diverse methods of ultrasound imaging.The most common modes are (a) B-mode -the basic two-dimensional intensity mode, (b) M-mode to assess moving body parts (e.g. cardiac movements) from the echoed sound and (c) Color mode pseudo coloring based on the detected cell motion. Ultrasound imaging is a generally utilized and safe restorative indicative system, because of its non intrusive nature, minimal effort and capacity of framing ongoing imaging. To accomplish the most ideal determination it is vital that medicinal pictures be sharp, clear and free of commotion and ancient rarities. However event of spot noise is an issue with ultrasound imaging. Spot is the curio caused by impedance of vitality from haphazardly appropriate...

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“…First, we applied the Lee filtering algorithm [Lee, 1986] to the time histories of the magnetic field amplitude and of the ocean transport components, in order to reduce adverse effects of statistical outliers in the time series. Parameters s (the threshold based on the standard deviation) and N (the size of the record segment) were assigned values 5 and 3, respectively, which led to the suppression of high-frequency noise on scales less than 2 months, but had little effect on the total variance and the spectral shape at frequencies below a 3-month (i.e., seasonal) timescale.…”

Section: Analysis Of Magnetic Field Variabilitymentioning

confidence: 99%

Variability of the ocean‐induced magnetic field predicted at sea surface and at satellite altitudes

Glazman

Golubev

2005

J. Geophys. Res.

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[1] Spatial and temporal variability of the magnetic field component induced by ocean circulation is investigated on the basis of a standard thin-shell approximation of electro-and magneto-static equations. Well-known difficulties of numerical solution of the governing equations are resolved by reducing the problem to an equation for the electric field potential, F, as opposed to a more conventional approach focused on the vertical jump, y, of the magnetic field potential across a combined ocean/ marine-sediment-layer spherical shell. The present formulation permits using more realistic input data on ocean currents and ultimately yields much greater (by at least an order of magnitude) values of the magnetic field at sea surface than predicted in earlier studies. Such large values are comparable to, and in some cases exceed, magnetic field variations caused by lithospheric and ionospheric sources on monthly to interannual timescales. At the 400-km altitude (of CHAMP satellite), the field attains 6 nT. The model predictions show favorable comparisons with some in situ measurements as well as with Challenging Minisatellite Payload (CHAMP) satellite magnetometer data.Citation: Glazman, R. E., and Y. N. Golubev (2005), Variability of the ocean-induced magnetic field predicted at sea surface and at satellite altitudes,

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Speckle Suppression And Analysis For Synthetic Aperture Radar Images

Cited by 440 publications

References 0 publications

Two-dimensional adaptive block Kalman filtering of SAR imagery

Two-dimensional adaptive block Kalman filtering of SAR imagery

A Review on Analysis of Speckle reduction in Ultra Sound Images

Variability of the ocean‐induced magnetic field predicted at sea surface and at satellite altitudes

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