An Adaptive Data-Fitting Model for Speckle Reduction of Log-Compressed Ultrasound Images
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Data Reduction
Speckle noise
In laser speckle contrast imaging, it was usually suggested that speckle size should exceed two camera pixels to eliminate the spatial averaging effect. In this work, we show the benefit of enhancing signal to noise ratio by correcting the speckle contrast at small speckle size. Through simulations and experiments, we demonstrated that local speckle contrast, even at speckle size much smaller than one pixel size, can be corrected through dividing the original speckle contrast by the static speckle contrast. Moreover, we show a 50% higher signal to noise ratio of the speckle contrast image at speckle size below 0.5 pixel size than that at speckle size of two pixels. These results indicate the possibility of selecting a relatively large aperture to simultaneously ensure sufficient light intensity and high accuracy and signal to noise ratio, making the laser speckle contrast imaging more flexible.
Speckle noise
Speckle imaging
Contrast-to-noise ratio
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This paper presents experimental work in which white-light illumination was used both for generating a speckle field in the recording process and for speckle noise suppression in the optical filtering process. In recording, a mechanically polished surface is clamped to a photoplate and is illuminated by an ordinary white-light source (such as a flashlight). The speckle field is generated by the interference of the partially coherent reflected wavelets. This speckle generation mechanism is different from the laser speckle method, in which highly coherent light illumination is necessary for speckle generation, and from the conventional white-light speckle method, in which a speckle pattern is either artificially created or naturally present. This speckle field is directly recorded on the photoplate without using any lens, resulting in sensitivity improvement. A double-exposure specklegram produced with this arrangement is then placed in an optical filtering processor with white-light illumination to yield information-carrying fringes. Speckle noise, which would appear strongly in the coherent optical filtering process, is effectively suppressed by this approach. This objective coherent white-light speckle method has many advantages. For example, there is no need for either a recording lens or a coherent light source. The specimen surface needs no special treatment, and it is capable of generating fringes with high sensitivity and low noise. Both theoretical analysis and applications of this method to practical problems are presented.
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It is shown that the speckle nature of electronic speckle pattern interferometry images can be reduced in quasireal time with the use of only three consecutive video images. By use of additive-subtractive phase modulation processing the technique is essentially insensitive to environmental noise, and distortion of fringes does not occur. By further removing the random speckle phase we show, using speckle statistics, that the number of dark speckles in the resulting fringe pattern is close to zero.
Speckle noise
Speckle imaging
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In this paper we introduce a new speckle suppression technique for medical ultrasound images that incorporates morphological properties of speckle as well as tissue classifying parameters. Each individual speckles is located, and, exploiting our prior knowledge on the tissue classification, it is determined whether this speckle is noise or a medically relevant detail. We apply the technique on images of neonatal brains affected by White Matter Damage (leukomalacia). The results show that applying an active contour on a processed image, in order to segment the affected areas, yields a segmentation much closer to that of an expert.
Speckle noise
Active contour model
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Removing speckle noise in electronic speckle pattern interferometry (ESPI) from a single speckle fringe pattern while keeping the fringe features is a difficult problem. The spin filtering with curved surface windows proposed by the authors is successful to filter out speckle noise nearly completely from a single speckle fringe pattern. However the new filtering has a difficulty to be overcome that the speckle fringe orientation map (SFOM) depends on the processing window size which is tryout and is difficult to be derived correctly when the speckle fringe density changes considerably. In this paper we utilize the original speckle pattern sequence with one-beam setup to determine the speckle movement direction field by digital correlation methods so that the SFOM is determined from the direction field. In this way the SFOM can be derived regardless of fringe density.
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A new promising method of speckle filtering is described and applied to both, synthetically generated and real SAR-images. The new method, which the authors call EPOS (Edge Preserving Optimized Speckle-filter), is based upon the statistical properties of speckle noise. The knowledge of speckle variance allows the distinction of homogeneous areas from those, containing edges or single scattering targets. This discrimination method is used within a variable sized filter matrix to choose a region, which is suitable for calculating an intensity average, that is typical for the center pixel. Also a method for estimating speckle noise parameters is presented.< >
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The deformation measurement method by using only two speckle patterns has been proposed in ESPI (electronic speckle pattern interferometry) by using Fourier transform. Furthermore, three-dimensional deformation of the object was able to be measured with the same sensitivities in each direction of three-dimensional axis. However, the measurement results of a complex shape deformation are not always a smooth distribution of phase map. It can be thought that this trouble is caused from the effect of speckle noise which is included in speckle pattern. In this paper, the solution of the problem concerning the speckle noise is investigated. The degradation of measurement accuracy in speckle interferometry is caused by some speckle noise. The speckle noise influences the bias component and the amplitude of the speckle pattern. Furthermore, the spatial movement of speckles of speckle-pattern during the deformation process also influences into the measurement accuracy. In this paper, the pre-treatment for the speckle interferometry is proposed in order to reduce such influence by speckle noise. In the experimental results, it is confirmed that the influence of speckle noise can be reduced by using the features of the reference and the object beams' intensity distributions in interference measurement process. The proposed method can reduce the influence of speckle noise to 1/1000 in comparing with the results of conventional method. The validity of the proposed method in the practical operation is confirmed from the experiments.
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Speckle imaging
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The characterisation and posterior detection of speckle noise in ultrasound (US) has been regarded as an important research topic in US imaging, mainly focusing on two specific applications: improving signal to noise ratio by removing speckle noise distribution and, secondly, detecting fully developed speckle patterns in order to perform a 3D reconstruction using only image content information from freehand sensorless images. The main novelty of this work is to show that speckle detection can be improved based on finding optimally discriminant low order speckle statistics. We describe a fully automatic method for speckle detection and propose and validate a framework to be efficiently applied to real B-scan data, not being published to date. Different experiments have been carried out in order to validate the speckle detection methodology using both real and simulated data.
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Speckle is a granular noise that inherently exists in all types of coherent imaging systems. The presence of speckle in an image reduces the resolution of the image and the detectability of the target. Many speckle reduction algorithms assume speckle noise is multiplicative. We instead model the speckle according to the exact physical process of coherent image formation. Thus, the model includes signal-dependent effects and accurately represents the higher order statistical properties of speckle that are important to the restoration procedure. Various adaptive restoration filters for intensity speckle images are derived based on different speckle model assumptions and a nonstationary image model. These filters respond adaptively to the signal-dependent speckle noise and the nonstationary statistics of the original image.
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Multiplicative noise
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