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Synthetic aperture radar (SAR) images map Earth’s surface at high resolution, regardless of the weather conditions or sunshine phenomena. Therefore, SAR images have applications in various fields. Speckle noise, which has the characteristic of multiplicative noise, degrades the image quality of SAR images, which causes information loss. This study proposes a speckle noise reduction algorithm while using the speckle reducing anisotropic diffusion (SRAD) filter, discrete wavelet transform (DWT), soft threshold, improved guided filter (IGF), and guided filter (GF), with the aim of removing speckle noise. First, the SRAD filter is applied to the SAR images, and a logarithmic transform is used to convert multiplicative noise in the resulting SRAD image into additive noise. A two-level DWT is used to divide the resulting SRAD image into one low-frequency and six high-frequency sub-band images. To remove the additive noise and preserve edge information, horizontal and vertical sub-band images employ the soft threshold; the diagonal sub-band images employ the IGF; while, the low- frequency sub-band image removes additive noise using the GF. The experiments used both standard and real SAR images. The experimental results reveal that the proposed method, in comparison to state-of-the art methods, obtains excellent speckle noise removal, while preserving the edges and maintaining low computational complexity.

Speckle contrast analysis, whether spatial or temporal, is a valuable optical technique extensively utilized in medical and engineering domains owing to its simplicity, affordability, and noninvasive nature. It relies on statistical analysis of the dynamic speckle pattern produced by the sample under examination, offering insights into the sample's dynamics. However, challenges persist in precisely measuring temporal speckle contrast, particularly for slow dynamic samples. Existing mathematical models fail to accurately reflect the experimental data, which could result in misinterpretation of the analyzed results. To overcome these constraints, we present a mathematical model that incorporates the correlation between adjacent pixels. We specifically concentrate on temporal correlation, i.e., the relationship between neighboring frames, to compute the temporal speckle contrast. We theoretically replicate the statistical analysis typically conducted to compute temporal speckle contrast in a series of consecutive raw speckle images. Unlike previous models, our calculations account for the potential correlation between neighboring pixels across successive frames. To validate this model, we apply it to the analysis of the dynamics of ATCC 25922 colonies. By considering the probable temporal correlation between neighboring pixels, the proposed model notably improves the precision of temporal speckle contrast measurements, particularly for slow dynamic samples. Analytical expressions for the contrast are derived, incorporating both Gaussian and Lorentzian correlation functions, which exhibit excellent agreement with experimental findings conducted on colonies. Conversely, for fast dynamic samples where neighboring pixels lack correlation, our model aligns with the outcomes of the previously reported models. The proposed model is well-suited for computing temporal contrast in both slow and fast dynamics, rendering it applicable to a wide range of biological and industrial systems.

When a biological tissue is illuminated with coherent light, an interference pattern will be formed at the detector, the so-called speckle pattern. Laser speckle contrast imaging (LSCI) is a technique based on the dynamic change in this backscattered light as a result of interaction with red blood cells. It can be used to visualize perfusion in various tissues and, even though this technique has been extensively described in the literature, the actual clinical implementation lags behind. We provide an overview of LSCI as a tool to image tissue perfusion. We present a brief introduction to the theory, review clinical studies from various medical fields, and discuss current limitations impeding clinical acceptance.

Significance: Noninvasive in vivo fast pulsatile blood flow measurement in deep tissue is important because the blood flow waveform is correlated with physiological parameters, such as blood pressure and elasticity of blood vessels. Compromised blood flow may cause diseases, such as stroke, foot ulcer, and myocardial ischemia. There is great clinical demand for a portable and cost-effective device for noninvasive pulsatile blood flow measurement. Aim: A diffuse-optics-based method, diffuse speckle pulsatile flowmetry (DSPF), was developed for fast measurement (∼300  Hz) of deep tissue blood flow noninvasively. To validate its performance, both a phantom experiment and in vivo demonstration were conducted. Approach: Over the past two decades, single-mode fibers have been used as detection fibers in most diffuse-optics-based deep tissue blood flow measurement modalities. We used a multimode (MM) detection fiber with a core size of 200  μm for diffused speckle pattern detection. A background intensity correction algorithm was implemented for speckle contrast calculation. The MM detection fiber helped to achieve a level of deep tissue blood flow measurement similar to that of conventional modalities, such as diffuse correlation spectroscopy and diffuse speckle contrast analysis, but it increases the measurement rate of blood flow to 300 Hz. Results: The design and implementation of the DSPF system were introduced. The theory of the background intensity correction for the diffused speckle pattern detected by the MM fiber was explained. A flow phantom was built for validation of the performance of the DSPF system. An in vivo cuff-induced occlusion experiment was performed to demonstrate the capability of the proposed DSPF system. Conclusions: An MM detection fiber can help to achieve fast (∼300  Hz) pulsatile blood flow measurement in the proposed DSPF method. The cost-effective device and the fiber-based flexible probe increase the usability of the DSPF system significantly.

Electronic speckle pattern interferometry (ESPI) is widely used in fields such as materials science, biomedical research, surface morphology analysis, and optical component inspection because of its high measurement accuracy, broad frequency range, and ease of measurement. Phase extraction is a critical stage in ESPI. However, conventional phase extraction methods exhibit problems such as low accuracy, slow processing speed, and poor generalization. With the continuous development of deep learning in image processing, the application of deep learning in phase extraction from electronic speckle interferometry images has become a critical topic of research. This paper reviews the principles and characteristics of ESPI and comprehensively analyzes the phase extraction processes for fringe patterns and wrapped phase maps. The application, advantages, and limitations of deep learning techniques in filtering, fringe skeleton line extraction, and phase unwrapping algorithms are discussed based on the representation of measurement results. Finally, this paper provides a perspective on future trends, such as the construction of physical models for electronic speckle interferometry, improvement and optimization of deep learning models, and quantitative evaluation of phase extraction quality, in this field.

Multi-exposure speckle imaging (MESI) is a label-free technique to visualize and measure blood flow. Accurate perfusion measurements are useful in a variety of applications, including surgery, monitoring treatment, and diagnosing various conditions. We aim to demonstrate the feasibility of capturing speckle images through an optical fiber bundle for use in MESI for potential applications such as endoscopy or where free space measurements are not feasible. To compare the accuracy of fiber bundle MESI measurements against free space MESI measurements, measurements of a tissue-simulating flow phantom and mouse cortex were acquired simultaneously through free space and an optical fiber bundle. Using the Pearson correlation coefficient for comparing measurements, values of 0.9994 and 0.9942 were calculated for low (1 to ) and high (10 to ) flow rates, respectively. For measurements, an value of 0.970 was calculated for flow in 14 vessels and 5 parenchyma regions. values of 0.953 and 0.906 were calculated for two vessels before, during, and after a stroke. MESI measurements through an optical fiber bundle show similar results to free-space MESI.

When the existing defect detection methods are used to detect the defects on the smooth surface of metal workpieces, there is a big difference between the detection results and the actual results, and the detection accuracy is low. Therefore, the digital speckle pattern interferometry (DSPI) technology is introduced in this paper to study the design of the detection method of smooth surface defects of the metal workpieces. In this paper, the smooth surface of metal workpiece is measured by using digital speckle interferometry. The speckle on the measurement image is processed by the Laplace algorithm, and the speckled area is automatically extracted; The defects on the smooth surface of the metal workpiece are detected and located according to the speckled area. The comparative experiments show that the detection errors of the two new detection methods are less than 0.1 µm in practical application, and the detection accuracy is high, which can realize the high-precision detection of smooth surface defects of metal workpieces.

In the realm of cerebrovascular monitoring, primary metrics typically include blood pressure, which influences cerebral blood flow (CBF) and is contingent upon vessel radius. Measuring CBF noninvasively poses a persistent challenge, primarily attributed to the difficulty of accessing and obtaining signal from the brain. Our study aims to introduce a compact speckle contrast optical spectroscopy device for noninvasive CBF measurements at long source-to-detector distances, offering cost-effectiveness, and scalability while tracking blood flow (BF) with remarkable sensitivity and temporal resolution. The wearable sensor module consists solely of a laser diode and a board camera. It can be easily placed on a subject's head to measure BF at a sampling rate of 80 Hz. Compared to the single-fiber-based version, the proposed device achieved a signal gain of about 70 times, showed superior stability, reproducibility, and signal-to-noise ratio for measuring BF at long source-to-detector distances. The device can be distributed in multiple configurations around the head. Given its cost-effectiveness, scalability, and simplicity, this laser-centric tool offers significant potential in advancing noninvasive cerebral monitoring technologies.

Surface roughness is commonly used to characterize material microstructure during processing, and accurate measurement of surface roughness is the premise and foundation of machining. Therefore, online non-destructive measurement of surface roughness based on the laser speckle method has become a hot issue in recent research. The improvements in surface roughness measurements based on the laser speckle method are systematically reviewed. Theory of speckle formation is introduced. The statistical properties of the speckle patterns including first-order statistical properties and second-order statistical properties are directly related to surface roughness. Surface roughness measurements based on the laser speckle method are roughly divided into the speckle contrast method, speckle correlation method, and fractal method. The three methods are described in detail, and an extensive comparison among all the methods is presented. The recent progresses and application of surface roughness measurements are reviewed. Finally, surface roughness measurements based on the laser speckle method are prospected and summarized.

Residual stresses are often overlooked in friction stir processing (FSP), but their significant impact on fatigue performance necessitates their consideration in optimizing processing parameters. The first step in this effort is understanding how process conditions influence residual stress distributions, especially across different alloys. This study focuses on determining and explaining the through-thickness residual stress variations and the effect of process temperature on the residual stress magnitude in wrought AA7075 and cast AA380.0 alloys. Additionally, for AA380.0, the impact of a second FSP pass was investigated. To achieve this, hole-drilling electronic speckle pattern interferometry (ESPI) and the thermal pseudo-mechanical (TPM) model within finite element analysis were employed to study the 3D distributions of in-plane residual stresses in processed samples under various conditions. A key finding was the varying impact of process temperatures on residual stress magnitudes. Higher process temperatures reduced stresses in AA380.0 but increased them in AA7075. Additionally, the through-thickness stress distributions differed between the two alloys. Further analysis revealed that yield stresses are crucial in explaining these phenomena and the effects of additional FSP passes. This fundamental understanding will be vital in guiding the efforts to mitigate residual stresses and assess their impact on the performance of FSP aluminum alloys.