Adaptive speckle filters and scene heterogeneity

Lopès, A.; Touzi, R.; Nezry, E.

doi:10.1109/36.62623

Cited by 818 publications

(385 citation statements)

References 11 publications

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“…So MMSE filter is widely used in speckle filtering. The coefficient of variation was chosen as a test of region characteristic by Lopes and the MMSE filter can be enhanced by comparing the test with two predefined thresholds [17]. The enhanced filter outputs average pixel value in a moving window for homogenous area and the central pixel for heterogeneous 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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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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“…The CV has been widely used to detect the scene heterogeneity, which was proposed to enhance the SAR filtering adaptively by Lopes et al [20].It is defined as the ratio between the standard deviation and the mean in a local region as follows: …”

Section: Scene Heterogeneitymentioning

confidence: 99%

Feature-Based Nonlocal Polarimetric SAR Filtering

et al. 2017

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Polarimetric synthetic aperture radar (PolSAR) images are inherently contaminated by multiplicative speckle noise, which complicates the image interpretation and image analyses. To reduce the speckle effect, several adaptive speckle filters have been developed based on the weighted average of the similarity measures commonly depending on the model or probability distribution, which are often affected by the distribution parameters and modeling texture components. In this paper, a novel filtering method introduces the coefficient of variance (CV) and Pauli basis (PB) to measure the similarity, and the two features are combined with the framework of the nonlocal mean filtering. The CV is used to describe the complexity of various scenes and distinguish the scene heterogeneity; moreover, the Pauli basis is able to express the polarimetric information in PolSAR image processing. This proposed filtering combines the CV and Pauli basis to improve the estimation accuracy of the similarity weights. Then, the similarity of the features is deduced according to the test statistic. Subsequently, the filtering is proceeded by using the nonlocal weighted estimation. The performance of the proposed filter is tested with the simulated images and real PolSAR images, which are acquired by AIRSAR system and ESAR system. The qualitative and quantitative experiments indicate the validity of the proposed method by comparing with the widely-used despeckling methods.

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“…The spatial resolution of all SAR images was 7m, and the size of all images was 1500 1000 pixels. After co-registering, all SAR images were filtered by an enhanced Lee filter with a -5 5-sized window, which has proved effective in reducing speckle noise (Lopes et al, 1990). The acquisition information of the datasets used (acquisition date, polarization, incidence angle) is presented in Table 1.…”

Section: Study Area and Datasetsmentioning

confidence: 99%

A Similarity Weight-based Method to Detect Damage Induced by a Tsunami

Jeon¹,

Kim²,

Kim³

2016

Journal of the Korean Society of Surveying, Geodesy, Photogramm

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Among the various remote sensing sensors compared to the electro-optical sensors, SAR (Synthetic Aperture Radar) is very suitable for assessing damaged areas induced by disaster events owing to its all-weather day and night acquisition capability and sensitivity to geometric variables. The conventional CD (Change Detection) method that uses two-date data is typically used for mapping damage over extensive areas in a short time, but because data from only two dates are used, the information used in the conventional CD is limited. In this paper, we propose a novel CD method that is extended to use data consisting of two pre-disaster SAR data and one post-disaster SAR data. The proposed CD method detects changes by using a similarity weight image derived from the neighborhood information of a pixel in the data from the three dates. We conducted an experiment using three single polarization ALOS PALSAR (Advanced Land Observing Satellite/Phased Array Type L-Band) data collected over Miyagi, Japan which was seriously damaged by the 2011 east Japan tsunami. The results demonstrated that the mapping accuracy for damaged areas can be improved by about 26% with an increase of the g-mean compared to the conventional CD method. These improved results prove the performance of our proposed CD method and show that the proposed CD method is more suitable than the conventional CD method for detecting damaged areas induced by disaster. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http:// creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

show abstract

Adaptive speckle filters and scene heterogeneity

Cited by 818 publications

References 11 publications

A Review on Analysis of Speckle reduction in Ultra Sound Images

A Review on Analysis of Speckle reduction in Ultra Sound Images

Feature-Based Nonlocal Polarimetric SAR Filtering

A Similarity Weight-based Method to Detect Damage Induced by a Tsunami

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