Showing papers on "Speckle noise published in 2014"

Non-invasive single-shot imaging through scattering layers and around corners via speckle correlations

Abstract: Optical imaging through and inside complex samples is a difficult challenge with important applications in many fields. The fundamental problem is that inhomogeneous samples such as biological tissue randomly scatter and diffuse light, preventing the formation of diffraction-limited images. Despite many recent advances, no current method can perform non-invasive imaging in real-time using diffused light. Here, we show that, owing to the ‘memory-effect’ for speckle correlations, a single high-resolution image of the scattered light, captured with a standard camera, encodes sufficient information to image through visually opaque layers and around corners with diffraction-limited resolution. We experimentally demonstrate single-shot imaging through scattering media and around corners using spatially incoherent light and various samples, from white paint to dynamic biological samples. Our single-shot lensless technique is simple, does not require wavefront-shaping nor time-gated or interferometric detection, and is realized here using a camera-phone. It has the potential to enable imaging in currently inaccessible scenarios. Diffraction-limited imaging in a variety of complex media is realized based on analysis of speckle correlations in light captured using a camera phone.

Depth-resolved model-based reconstruction of attenuation coefficients in optical coherence tomography

Abstract: We present a method, based on a single scattering model, to calculate the attenuation coefficient of each pixel in optical coherence tomography (OCT) depth profiles. Numerical simulations were used to determine the model’s response to different depths and attenuation coefficients. Experiments were performed on uniform and layered phantoms with varying attenuation coefficients. They were measured by a 1300 nm OCT system and their attenuation coefficients were evaluated by our proposed method and by fitting the OCT slope as the gold standard. Both methods showed largely consistent results for the uniform phantoms. On the layered phantom, only our proposed method accurately estimated the attenuation coefficients. For all phantoms, the proposed method largely reduced the variability of the estimated attenuation coefficients. The method was illustrated on an in-vivo retinal OCT scan, effectively removing common imaging artifacts such as shadowing. By providing localized, per-pixel attenuation coefficients, this method enables tissue characterization based on attenuation coefficient estimates from OCT data.

Fuzzy Clustering With a Modified MRF Energy Function for Change Detection in Synthetic Aperture Radar Images

Abstract: In this paper, we put forward a novel approach for change detection in synthetic aperture radar (SAR) images. The approach classifies changed and unchanged regions by fuzzy c-means (FCM) clustering with a novel Markov random field (MRF) energy function. In order to reduce the effect of speckle noise, a novel form of the MRF energy function with an additional term is established to modify the membership of each pixel. In addition, the degree of modification is determined by the relationship of the neighborhood pixels. The specific form of the additional term is contingent upon different situations, and it is established ultimately by utilizing the least-square method. There are two aspects to our contributions. First, in order to reduce the effect of speckle noise, the proposed approach focuses on modifying the membership instead of modifying the objective function. It is computationally simple in all the steps involved. Its objective function can just return to the original form of FCM, which leads to its consuming less time than that of some obviously recently improved FCM algorithms. Second, the proposed approach modifies the membership of each pixel according to a novel form of the MRF energy function through which the neighbors of each pixel, as well as their relationship, are concerned. Theoretical analysis and experimental results on real SAR datasets show that the proposed approach can detect the real changes as well as mitigate the effect of speckle noises. Theoretical analysis and experiments also demonstrate its low time complexity.

BRINT: Binary Rotation Invariant and Noise Tolerant Texture Classification

Abstract: In this paper, we propose a simple, efficient, yet robust multiresolution approach to texture classification—binary rotation invariant and noise tolerant (BRINT). The proposed approach is very fast to build, very compact while remaining robust to illumination variations, rotation changes, and noise. We develop a novel and simple strategy to compute a local binary descriptor based on the conventional local binary pattern (LBP) approach, preserving the advantageous characteristics of uniform LBP. Points are sampled in a circular neighborhood, but keeping the number of bins in a single-scale LBP histogram constant and small, such that arbitrarily large circular neighborhoods can be sampled and compactly encoded over a number of scales. There is no necessity to learn a texton dictionary, as in methods based on clustering, and no tuning of parameters is required to deal with different data sets. Extensive experimental results on representative texture databases show that the proposed BRINT not only demonstrates superior performance to a number of recent state-of-the-art LBP variants under normal conditions, but also performs significantly and consistently better in presence of noise due to its high distinctiveness and robustness. This noise robustness characteristic of the proposed BRINT is evaluated quantitatively with different artificially generated types and levels of noise (including Gaussian, salt and pepper, and speckle noise) in natural texture images.

Exploiting Patch Similarity for SAR Image Processing: The nonlocal paradigm

Abstract: Most current synthetic aperture radar (SAR) systems offer high-resolution images featuring polarimetric, interferometric, multifrequency, multiangle, or multidate information. SAR images, however, suffer from strong fluctuations due to the speckle phenomenon inherent to coherent imagery. Hence, all derived parameters display strong signal-dependent variance, preventing the full exploitation of such a wealth of information. Even with the abundance of despeckling techniques proposed over the last three decades, there is still a pressing need for new methods that can handle this variety of SAR products and efficiently eliminate speckle without sacrificing the spatial resolution. Recently, patch-based filtering has emerged as a highly successful concept in image processing. By exploiting the redundancy between similar patches, it succeeds in suppressing most of the noise with good preservation of texture and thin structures. Extensions of patch-based methods to speckle reduction and joint exploitation of multichannel SAR images (interferometric, polarimetric, or PolInSAR data) have led to the best denoising performance in radar imaging to date. We give a comprehensive survey of patch-based nonlocal filtering of SAR images, focusing on the two main ingredients of the methods: measuring patch similarity and estimating the parameters of interest from a collection of similar patches.

Fast Adaptive Nonlocal SAR Despeckling

Abstract: Despeckling techniques based on the nonlocal approach provide an excellent performance, but exhibit also a remarkable complexity, unsuited to time-critical applications. In this letter, we propose a fast nonlocal despeckling filter. Starting from the recent SAR-BM3D algorithm, we propose to use a variable-size search area driven by the activity level of each patch, and a probabilistic early termination approach that exploits speckle statistics in order to speed up block matching. Finally, the use of look-up tables helps in further reducing the processing costs. The technique proposed conjugates excellent performance and low complexity, as demonstrated on both simulated and real-world SAR images and on a dedicated SAR despeckling benchmark.

The influence of optical fiber bundle parameters on the transmission of laser speckle patterns

Abstract: Laser speckle imaging (LSI) techniques provide important functional information about tissue perfusion and mechanical properties. To perform LSI in vivo, laser speckle patterns are transmitted via optical fiber bundles incorporated within small-diameter endoscopes. Inter-fiber crosstalk due to mode coupling in fiber bundles can result in erroneous speckle statistics and therefore reduces the accuracy of LSI analysis. In this paper, we investigate the influence of multiple parameters that influence crosstalk between neighboring cores within optical fiber bundles and govern the modulation of transmitted laser speckle patterns. Our results show that in addition to large core-to-core separation, large refractive index contrast between core and cladding material, reduced number of propagating modes and variability in core size are essential parameters for accurate speckle pattern transmission to conduct endoscopic LSI.

Using Combined Difference Image and $k$ -Means Clustering for SAR Image Change Detection

Abstract: In this letter, a simple and effective unsupervised approach based on the combined difference image and k-means clustering is proposed for the synthetic aperture radar (SAR) image change detection task. First, we use one of the most popular denoising methods, the probabilistic-patch-based algorithm, for speckle noise reduction of the two multitemporal SAR images, and the subtraction operator and the log ratio operator are applied to generate two kinds of simple change maps. Then, the mean filter and the median filter are used to the two change maps, respectively, where the mean filter focuses on making the change map smooth and the local area consistent, and the median filter is used to preserve the edge information. Second, a simple combination framework which uses the maps obtained by the mean filter and the median filter is proposed to generate a better change map. Finally, the k-means clustering algorithm with k = 2 is used to cluster it into two classes, changed area and unchanged area. Local consistency and edge information of the difference image are considered in this method. Experimental results obtained on four real SAR image data sets confirm the effectiveness of the proposed approach.

Speckle contrast optical spectroscopy, a non-invasive, diffuse optical method for measuring microvascular blood flow in tissue

Abstract: We introduce a new, non-invasive, diffuse optical technique, speckle contrast optical spectroscopy (SCOS), for probing deep tissue blood flow using the statistical properties of laser speckle contrast and the photon diffusion model for a point source. The feasibility of the method is tested using liquid phantoms which demonstrate that SCOS is capable of measuring the dynamic properties of turbid media non-invasively. We further present an in vivo measurement in a human forearm muscle using SCOS in two modalities: one with the dependence of the speckle contrast on the source-detector separation and another on the exposure time. In doing so, we also introduce crucial corrections to the speckle contrast that account for the variance of the shot and sensor dark noises.

Analysis, Evaluation, and Comparison of Polarimetric SAR Speckle Filtering Techniques

Abstract: Speckle noise filtering on polarimetric SAR (PolSAR) images remains a challenging task due to the difficulty to reduce a scatterer-dependent noise while preserving the polarimetric information and the spatial information. This challenge is particularly acute on single look complex images, where little information about the scattering process can be derived from a rank-1 covariance matrix. This paper proposes to analyze and to evaluate the performances of a set of PolSAR speckle filters. The filter performances are measured by a set of ten different indicators, including relative errors on incoherent target decomposition parameters, coherences, polarimetric signatures, point target, and edge preservation. The result is a performance profile for each individual filter. The methodology consists of simulating a set of artificial PolSAR images on which the various filters will be evaluated. The image morphology is stochastic and determined by a Markov random field and the number of scattering classes is allowed to vary so that we can explore a large range of image configurations. Evaluation on real PolSAR images is also considered. Results show that filters performances need to be assessed using a complete set of indicators, including distributed scatterer parameters, radiometric parameters, and spatial information preservation.

Artificial Neural Network Application in the Diagnosis of Disease Conditions with Liver Ultrasound Images

Abstract: The preliminary study presented within this paper shows a comparative study of various texture features extracted from liver ultrasonic images by employing Multilayer Perceptron (MLP), a type of artificial neural network, to study the presence of disease conditions. An ultrasound (US) image shows echo-texture patterns, which defines the organ characteristics. Ultrasound images of liver disease conditions such as “fatty liver,” “cirrhosis,” and “hepatomegaly” produce distinctive echo patterns. However, various ultrasound imaging artifacts and speckle noise make these echo-texture patterns difficult to identify and often hard to distinguish visually. Here, based on the extracted features from the ultrasonic images, we employed an artificial neural network for the diagnosis of disease conditions in liver and finding of the best classifier that distinguishes between abnormal and normal conditions of the liver. Comparison of the overall performance of all the feature classifiers concluded that “mixed feature set” is the best feature set. It showed an excellent rate of accuracy for the training data set. The gray level run length matrix (GLRLM) feature shows better results when the network was tested against unknown data.

Speckle contrast optical tomography: A new method for deep tissue three-dimensional tomography of blood flow.

Abstract: A novel tomographic method based on the laser speckle contrast, speckle contrast optical tomography (SCOT) is introduced that allows us to reconstruct three dimensional distribution of blood flow in deep tissues. This method is analogous to the diffuse optical tomography (DOT) but for deep tissue blood flow. We develop a reconstruction algorithm based on first Born approximation to generate three dimensional distribution of flow using the experimental data obtained from tissue simulating phantoms.

Encoding multiple holograms for speckle-noise reduction in optical display.

Abstract: In digital holography (DH) a mixture of speckle and incoherent additive noise, which appears in numerical as well as in optical reconstruction, typically degrades the information of the object wavefront. Several methods have been proposed in order to suppress the noise contributions during recording or even during the reconstruction steps. Many of them are based on the incoherent combination of multiple holographic reconstructions achieving remarkable improvement, but only in the numerical reconstruction i.e. visualization on a pc monitor. So far, it has not been shown the direct synthesis of a digital hologram which provides the denoised optical reconstruction. Here, we propose a new effective method for encoding in a single complex wavefront the contribution of multiple incoherent reconstructions, thus allowing to obtain a single synthetic digital hologram that show significant speckle-reduction when optically projected by a Spatial Light Modulator (SLM).

High-temperature digital image correlation method for full-field deformation measurement captured with filters at 2600°C using spraying to form speckle patterns

Abstract: A method is presented for obtaining good images of a sprayed speckle pattern on specimen surfaces at high temperatures, suitable for strain measurement, by digital image correlation (DIC) using plasma spray for speckle preparation in which a bandpass filter, neutral density filters, and a linear polarizing filter are used to reduce intensity and noise in images. This is accomplished by speckle preparation through the use of plasma spray and suppression of black-body radiation through the use of filters. By using plasma spray for speckle preparation and the filters for image acquisition, the method was demonstrated to be capable of providing accurate DIC measurements up to 2600°C. The full-field stretching deformation of the specimen was determined using the DIC technique. Experimental results indicate that the proposed high-temperature DIC method is easy to implement and can be applied to practical, full-field, high-temperature deformation measurements with high accuracy.

All about speckles: Speckle size measurement

Abstract: 2Sutton, D.A., J.J. Orteu, and H.W. Schreier, Image Correlation for Shape, Motion and Deformation Measurements. 2009, New York, NY: Springer. 3Lecompte, D., et al., Optics and Lasers in Engineering, 2006. 44(11): p. 1132–1145. 4Or it should! the very important issue of aliased speckles. It is difficult to tell once imaged, whether your speckles are aliased, but if you have guaranteed that they are not, it is helpful to measure the average speckle size and distribution. This article discusses methods of doing just that.

Random super-prism wavelength meter

Abstract: The speckle pattern arising from a thin random, disordered scatterer may be used to detect the transversal mode of an incident beam. On the other hand, speckle patterns originating from meter-long multimode fibers can be used to detect different wavelengths. Combining these approaches, we develop a method that uses a thin random scattering medium to measure the wavelength of a near-infrared laser beam with picometer resolution. The method is based on the application of principal component analysis, which is used for pattern recognition and is applied here to the case of speckle pattern categorization.

Speckle Reduction via Higher Order Total Variation Approach

Abstract: Multiplicative noise (also known as speckle) reduction is a prerequisite for many image-processing tasks in coherent imaging systems, such as the synthetic aperture radar. One approach extensively used in this area is based on total variation (TV) regularization, which can recover significantly sharp edges of an image, but suffers from the staircase-like artifacts. In order to overcome the undesirable deficiency, we propose two novel models for removing multiplicative noise based on total generalized variation (TGV) penalty. The TGV regularization has been mathematically proven to be able to eliminate the staircasing artifacts by being aware of higher order smoothness. Furthermore, an efficient algorithm is developed for solving the TGV-based optimization problems. Numerical experiments demonstrate that our proposed methods achieve state-of-the-art results, both visually and quantitatively. In particular, when the image has some higher order smoothness, our methods outperform the TV-based algorithms.

MIMOSA: An Automatic Change Detection Method for SAR Time Series

Abstract: This paper presents a new automatic change detection technique for synthetic aperture radar (SAR) time series, i.e., Method for generalIzed Means Ordered Series Analysis (MIMOSA). The method compares only two different temporal means between the amplitude images, whatever the length of the time series. The method involves three different steps: 1) estimation of the amplitude distribution parameters over the images; 2) computation of the theoretical joint probability density function between the two temporal means; and 3) automatic thresholding according to a given false alarm rate, which is the only change detection parameter. The procedure is executed with a very low computational cost and does not require any spatial speckle filtering. Indeed, the full image resolution is used. Due to the temporal means, the data volume to process is reduced, which is very helpful. Moreover, the two means can be simply updated using the new incoming images only. Thus, the full time series is not processed again. Change detection results between image pairs are presented with the airborne sensor CARABAS-II, using a public data release, and with TerraSAR-X data. In the case of time series, change detection results are illustrated using a TerraSAR-X time series. In every case, the MIMOSA method produces very good results.

Speckle statistics in OCT images: Monte Carlo simulations and experimental studies

Abstract: The speckle pattern of an optical coherence tomography (OCT) image carries potentially useful sample information that may assist in tissue characterization. Recent biomedical results in vivo indicate that the distribution of signal intensities within an OCT tissue image is well described by a log-normal-like (Gamma) function. To fully understand and exploit this finding, an OCT Monte Carlo model that accounts for speckle effects was developed. The resultant Monte Carlo speckle statistics predictions agree well with experimental OCT results from a series of control phantoms with variable scattering properties; the Gamma distribution provides a good fit to the theoretical and experimental results. The ability to quantify subresolution tissue features via OCT speckle analysis may prove useful in diagnostic photomedicine.

Hybrid Approach for Unbiased Coherence Estimation for Multitemporal InSAR

Abstract: The coherence of radar echoes is a fundamental observable in interferometric synthetic aperture radar (InSAR) measurements. It provides a quantitative measure of the scattering properties of imaged surfaces and therefore is widely applied to study the physical processes of the Earth. However, unfortunately, the estimated coherence values are often biased due to various reasons such as radar signal nonstationarity and the bias in the estimators used. In this paper, we focus on multitemporal InSAR coherence estimation and present a hybrid approach that mitigates effectively the errors in the estimation. The proposed approach is almost completely self-adaptive and workable for both Gaussian and non-Gaussian SAR scenes. Moreover, the bias of the sample coherence can be mitigated with even only several samples included for a given pixel. Therefore, it is a more pragmatic method for accurate coherence estimation and can be applied actually. Different data sets are used to test the proposed method and demonstrate its advantages.

Robust Polarimetric SAR Despeckling Based on Nonlocal Means and Distributed Lee Filter

Abstract: This paper presents a nonlocal Lee (NL-Lee) filter for polarimetric synthetic aperture radar despeckling. In the proposed NL-Lee filter, a kind of hybrid patch similarity measure is constructed by combining together the structure similarity introduced by the nonlocal means (NLM) filter and the homogeneity similarity introduced by the Lee filter, which works in a distributive way. This combination leads to two important advantages for the proposed nonlocal filter. One is the improved robustness to the NLM parameters such as patch size and search neighborhood size, since the patch regularity assumption can be enhanced by the introduced hybrid patch similarity; the other one is the better performance tradeoff between speckle removal and detail preservation, because of the good balance between structure similarity and homogeneity similarity obtained by the framework of the proposed NL-Lee filter. Experimental results are given to demonstrate its competitive denoising performance.

Polarimetric SAR Image Segmentation Using Statistical Region Merging

Abstract: The statistical region merging (SRM) algorithm exhibits efficient performance in solving significant noise corruption and does not depend on the data distribution. These advantages make SRM suitable for the segmentation of synthetic aperture radar (SAR) images, which are characterized by speckle noise and different distributions of various data types and spatial resolutions. However, the original SRM algorithm is designed for RGB and gray images characterized by additive noise and having a range of [0, 255]. In this letter, the SRM algorithm is generalized so that it can be applied to images with larger range and multiplicative noise. The original 4-neighborhood models are also generalized into 8-neighborhood models. The effectiveness of the generalized SRM (GSRM) algorithm is demonstrated by AirSAR and ESAR L-band Polarimetric SAR (PolSAR) data. Given that the input data of the GSRM algorithm can be single- or multi-dimensional, the proposed GSRM algorithm can be used for single- and multi-polarized as well as for fully polarimetric SAR data.

Accuracy of Incoherent Speckle Tracking for Circular Gaussian Signals

Abstract: This letter provides a formula for the accuracy of incoherent speckle tracking (intensity cross-correlation) of homogeneous patches. The result is based on the determination of the curvature of the cross-correlation function and the noise which affects its first derivative.

Rapid quantitative phase imaging for partially coherent light microscopy

Abstract: Partially coherent light provides promising advantages for imaging applications. In contrast to its completely coherent counterpart, it prevents image degradation due to speckle noise and decreases cross-talk among the imaged objects. These facts make attractive the partially coherent illumination for accurate quantitative imaging in microscopy. In this work, we present a non-interferometric technique and system for quantitative phase imaging with simultaneous determination of the spatial coherence properties of the sample illumination. Its performance is experimentally demonstrated in several examples underlining the benefits of partial coherence for practical imagining applications. The programmable optical setup comprises an electrically tunable lens and sCMOS camera that allows for high-speed measurement in the millisecond range.

Speckle reduction in holographic projection by random pixel separation with time multiplexing.

Abstract: A speckle-reduction method with random locations of sparse object points is proposed for image quality improvement based on a time-multiplexing approach in holographic reconstruction. The object points of a reconstructed image are divided into groups of sparse object points. Pixel separation of the periodic location, in general, is used for the sparse object points. However, an unwanted periodic fringe pattern is caused, and it dominantly degrades the reconstructed image quality. The proposed random pixel separation enables the reconstructed image quality to improve more effectively. The numerical simulation and the optical experiment are presented to confirm the performance of the proposed method.

Bayesian Shearlet shrinkage for SAR image de-noising via sparse representation

Abstract: As SAR has been widely used nearly in every field, how to improve SAR's image in both quality and visual effect has become necessary. Before what we really process the SAR image like image segmentation, edge detection, target detection or other processing, we must suppress the speckle noise in the image firstly. By analyzing the sorts and origins of noises, we present a new de-noising method of SAR image in the Shearlet domain based on sparse representation and Bayesian theory. Firstly, we apply the Shearlet transform to the noised SAR image. Secondly, we construct a new de-noising model via sparse representation and then use iterative algorithm based on Bayesian theory to solve it. Lastly, we can obtain the clean SAR image from the de-nosing Shearlet coefficients. The experimental results show that the proposed algorithm can not only effectively suppress speckle noise to improve the PSNR of SAR image, but also significantly improves the visual effect of SAR image, especially in enhancing the image's texture.

Two-Step Multitemporal Nonlocal Means for Synthetic Aperture Radar Images

Abstract: This paper presents a denoising approach for multitemporal synthetic aperture radar (SAR) images based on the concept of nonlocal means (NLM). It exploits the information redundancy existing in multitemporal images by a two-step strategy. The first step realizes a nonlocal weighted estimation driven by the redundancy in time, whereas the second step makes use of the nonlocal estimation in space. Using patch similarity miss-registration estimation, we also adapted this approach to the case of unregistered SAR images. The experiments illustrate the efficiency of the proposed method to denoise multitemporal images while preserving new information.

Quantitative technique for robust and noise-tolerant speed measurements based on speckle decorrelation in optical coherence tomography

Abstract: Intensity-based techniques in optical coherence tomography (OCT), such as those based on speckle decorrelation, have attracted great interest for biomedical and industrial applications requiring speed or flow information. In this work we present a rigorous analysis of the effects of noise on speckle decorrelation, demonstrate that these effects frustrate accurate speed quantitation, and propose new techniques that achieve quantitative and repeatable measurements. First, we derive the effect of background noise on the speckle autocorrelation function, finding two detrimental effects of noise. We propose a new autocorrelation function that is immune to the main effect of background noise and permits quantitative measurements at high and moderate signal-to-noise ratios. At the same time, this autocorrelation function is able to provide motion contrast information that accurately identifies areas with movement, similar to speckle variance techniques. In order to extend the SNR range, we quantify and model the second effect of background noise on the autocorrelation function through a calibration. By obtaining an explicit expression for the decorrelation time as a function of speed and diffusion, we show how to use our autocorrelation function and noise calibration to measure a flowing liquid. We obtain accurate results, which are validated by Doppler OCT, and demonstrate a very high dynamic range (> 600 mm/s) compared to that of Doppler OCT (±25 mm/s). We also derive the behavior for low flows, and show that there is an inherent non-linearity in speed measurements in the presence of diffusion due to statistical fluctuations of speckle. Our technique allows quantitative and robust measurements of speeds using OCT, and this work delimits precisely the conditions in which it is accurate.