Toward a fast and non-darkroom solution for speckle correlation based scattering imaging
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Darkroom
Speckle imaging
Speckle noise
Speckle-Modulating Optical Coherence Tomography (SM-OCT) is a method based on light manipulation for removing speckle noise without significant loss of resolution. By removing speckle noise, SM-OCT reveals small structures in the tissues of living animals.
Speckle noise
Optical Tomography
Speckle imaging
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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.
Speckle noise
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Speckle imaging
Speckle noise
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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
Distortion (music)
Subtractive color
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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.
Speckle noise
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In ultrasound images a special type of acoustic noise, technically known as speckle noise, is the major factor of image quality degradation. In order to improve the image quality by means of speckle suppression and thus to increase the diagnostic potential of medical ultrasound, it is important to quantify the speckle. This paper describes, quality metrics for speckle in coherent imaging and their limitations. It also describes a new metric-SDI, its uniqueness in quantifying the speckle and comparison of performance with existing metrics. Empirical verification of SDI with a set of Test Images proves its speckle quantification in ultrasound. A subjective criterion is also taken into account to support the results.
Speckle noise
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Removing speckle noise of electronic speckle pattern interferometry (ESPI) from a single ESPI fringe pattern while keeping the fringe feature is a difficult problem and remains unsolved. In this paper, the spin filtering proposed by the authors is developed further with curved surface windows to filter off speckle noise from a single speckle fringe pattern. With the new filtering, the speckle noise can be removed nearly completely from a single speckle fringe pattern. The most important advantage of the method is that no blurring effects occur for speckle fringe patterns and the smooth fringe pattern of a phase field is retrieved and derived.
Speckle noise
Spatial filter
Speckle imaging
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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.
Speckle noise
Multiplicative noise
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