Speckle Reduction Research Articles

An Efficient Multi-Scale Wavelet Approach for Dehazing and Denoising Ultrasound Images Using Fractional-Order Filtering

Ultrasound imaging is widely used in medical diagnostics due to its non-invasive and real-time capabilities. However, existing methods often overlook the benefits of fractional-order filters for denoising and dehazing. Thus, this work introduces an efficient multi-scale wavelet method for dehazing and denoising ultrasound images using a fractional-order filter, which integrates a guided filter, directional filter, fractional-order filter, and haze removal to the different resolution images generated by a multi-scale wavelet. In the directional filter stage, an eigen-analysis of each pixel is conducted to extract structural features, which are then classified into edges for targeted filtering. The guided filter subsequently reduces speckle noise in homogeneous anatomical regions. The fractional-order filter allows the algorithm to effectively denoise while improving edge definition, irrespective of the edge size. Haze removal can effectively eliminate the haze caused by attenuation. Our method achieved significant improvements, with PSNR reaching 31.25 and SSIM 0.905 on our ultrasound dataset, outperforming other methods. Additionally, on external datasets like McMaster and Kodak24, it achieved the highest PSNR (29.68, 28.62) and SSIM (0.858, 0.803). Clinical evaluations by four radiologists confirmed its superiority in liver and carotid artery images. Overall, our approach outperforms existing speckle reduction and structural preservation techniques, making it highly suitable for clinical ultrasound imaging.

SAR-NTV-YOLOv8: A Neural Network Aircraft Detection Method in SAR Images Based on Despeckling Preprocessing

Monitoring aircraft using synthetic aperture radar (SAR) images is a very important task. Given its coherent imaging characteristics, there is a large amount of speckle interference in the image. This phenomenon leads to the scattering information of aircraft targets being masked in SAR images, which is easily confused with background scattering points. Therefore, automatic detection of aircraft targets in SAR images remains a challenging task. For this task, this paper proposes a framework for speckle reduction preprocessing of SAR images, followed by the use of an improved deep learning method to detect aircraft in SAR images. Firstly, to improve the problem of introducing artifacts or excessive smoothing in speckle reduction using total variation (TV) methods, this paper proposes a new nonconvex total variation (NTV) method. This method aims to ensure the effectiveness of speckle reduction while preserving the original scattering information as much as possible. Next, we present a framework for aircraft detection based on You Only Look Once v8 (YOLOv8) for SAR images. Therefore, the complete framework is called SAR-NTV-YOLOv8. Meanwhile, a high-resolution small target feature head is proposed to mitigate the impact of scale changes and loss of depth feature details on detection accuracy. Then, an efficient multi-scale attention module was proposed, aimed at effectively establishing short-term and long-term dependencies between feature grouping and multi-scale structures. In addition, the progressive feature pyramid network was chosen to avoid information loss or degradation in multi-level transmission during the bottom-up feature extraction process in Backbone. Sufficient comparative experiments, speckle reduction experiments, and ablation experiments are conducted on the SAR-Aircraft-1.0 and SADD datasets. The results have demonstrated the effectiveness of SAR-NTV-YOLOv8, which has the most advanced performance compared to other mainstream algorithms.

Wavelet Transform and Thresholding Techniques in Medical Ultrasound Image Speckle Reduction

Wavelet Transform and Thresholding Techniques in Medical Ultrasound Image Speckle Reduction

Speckle reduction in digital holography with Non-local means filter based on the structural similarity

Speckle noise limits the application of digital holography across various fields. This paper proposes a Non-local means filter method to suppress speckle noise by determining the weight of each traversed pixel based on the structural similarity between image blocks centered on the target and traversed pixels. Experimental results show that, compared to other typical digital image processing methods, this method can significantly reduce the speckle noise and exhibits a clear advantage across various metrics. These results indicate that the proposed method holds significant development potential in the field of speckle noise reduction.

A Power Law Reconstruction of Ultrasound Backscatter Images

Ultrasound B-scan images are traditionally formed from the envelope of the received radiofrequency echoes, but the image texture is dominated by granular speckle patterns. Longstanding efforts at speckle reduction and deconvolution have been developed to lessen the detrimental aspects of speckle. However, we now propose an alternative approach to estimation (and image rendering) of the underlying fine grain scattering density of tissues based on power law constraints. The key steps are a whitening of the spectrum of the received signal while conforming to the original envelope shape and statistics, followed by a power law filtering in accordance with the known scattering behavior of tissues. This multiple step approach results in a high-spatial-resolution map of scattering density that is constrained by the most important properties of scattering from tissues. Examples from in vivo liver scans are shown to illustrate the change in image properties from this framework.

Collaborative masking based speckle disentanglement for self-supervised optical coherence tomography image despeckling

Optical coherence tomography (OCT) has proven to be an effective and safe diagnostic tool in clinical settings due to its unique advantages. Nonetheless, the OCT images are unavoidably corrupted with speckle noise. Despeckling has become a vital preliminary step in OCT based image processing and analysis tasks. The supervised deep learning (DL) based denoising algorithms have recently attracted much attention due to their promising performance, but the lack of the noise-free OCT images renders it difficult to provide the effective supervision for DL model training. This paper has presented a self-supervised OCT image despeckling approach which does not depend on the reference clean image and combines the disentangled representation strategy with speckle noise estimation for the effective denoising. The proposed algorithm firstly disentangles the corrupted image into the speckle noise and clean image. Then, the multiple generated clean images are multiplied by the disentangled speckle noise to synthesize the speckle-corrupted image for self-supervised learning. To make the generated clean image and extracted speckle noise more statistically realistic, a collaborative masking strategy and a noise calibration module are applied to explore the spatial correlation of the clean image and refine speckle noise. The proposed method has been validated on two public datasets. Experimental results demonstrate that our method has superior speckle reduction performance to some traditional and DL-based despeckling approaches in terms of visual evaluation and quantitative metrics. Particularly, our method provides contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) improvements of at least 0.35 dB and 14.37 dB over the compared methods, respectively. Additionally, the retinal image classification results provide further evidence for the exceptional despeckling performance of our approach against other compared methods. It is obvious that the developed method has significant advantages in keeping the fidelity of retinal structure and enhancing the speckle suppression level. Meanwhile, the presented method holds certain reference value for improving diagnostic efficiency in clinical.

Speckle reduction in retrieved phase using digital holography with transport of intensity

Speckle reduction in retrieved phase using digital holography with transport of intensity

Continuous-wave degenerate cavity laser for optical imaging in scattering media.

Lasers play a significant role in optical communication, medical, and scientific research, owing to their high brightness and high coherence. However, the high spatial coherence will lead to specific challenges, such as speckle noise in imaging and wavefront distortion during propagation through scattering media. Here, a continuous-wave (cw) degenerate cavity laser (DCL) with low spatial coherence is demonstrated with efficient suppression of the thermal lensing effect from the gain crystal. Experimentally, a cw degenerate laser output with about 2000 transverse modes corresponding to a speckle contrast of about 0.0224 is achieved. This laser can be used for speckle reduction and is robust against atmospheric turbulence, which may find applications in the field of laser imaging technology and illumination.

Probabilistic volumetric speckle suppression in OCT using deep learning.

We present a deep learning framework for volumetric speckle reduction in optical coherence tomography (OCT) based on a conditional generative adversarial network (cGAN) that leverages the volumetric nature of OCT data. In order to utilize the volumetric nature of OCT data, our network takes partial OCT volumes as input, resulting in artifact-free despeckled volumes that exhibit excellent speckle reduction and resolution preservation in all three dimensions. Furthermore, we address the ongoing challenge of generating ground truth data for supervised speckle suppression deep learning frameworks by using volumetric non-local means despeckling-TNode- to generate training data. We show that, while TNode processing is computationally demanding, it serves as a convenient, accessible gold-standard source for training data; our cGAN replicates efficient suppression of speckle while preserving tissue structures with dimensions approaching the system resolution of non-local means despeckling while being two orders of magnitude faster than TNode. We demonstrate fast, effective, and high-quality despeckling of the proposed network in different tissue types that are not part of the training. This was achieved with training data composed of just three OCT volumes and demonstrated in three different OCT systems. The open-source nature of our work facilitates re-training and deployment in any OCT system with an all-software implementation, working around the challenge of generating high-quality, speckle-free training data.

Diffraction calculations from real-to-complex, complex-to-real, and real-to-real fields

Conventional diffraction calculations typically employ complex Fourier transforms in which the source and target fields are represented by complex values. However, this approach is inefficient for certain applications. To address this problem, this study introduces diffraction calculations for three fields: real-to-complex, complex-to-real, and real-to-real. These calculations utilize a real-valued fast Fourier transform and Hermite symmetry, enabling accelerated computation by eliminating half of the spectra. This study also demonstrates the practical applications of these diffraction calculations. These applications include image reproduction in digital holography, speckle reduction in holographic projection, and accelerated hologram computation in holographic displays.

Adaptive Bayesian speckle reduction in the 2D DOST domain using 2D MC-GARCH-M model: Preserving image edges and textural information

Image denoising and image quality enhancement are major issues in image processing. Additive and multiplicative noise removal is one of the main image enhancement approaches. In this paper, we propose a novel 2D Merged Complex Generalized Autoregressive Conditional Heteroscedasticity Mixture (MC-GARCH-M) model for 2D complex stochastic processes. This model effectively captures non-Gaussian statistics and all three types of dependencies within the processes (RRD, IID, and RID) using the proposed linear mapping transformation T. Our statistical analysis shows that the 2D MC-GARCH-M model provides the best fit to the non-Gaussian statistics of the mapped matrix of 2D DOST coefficients compared to Gaussian, Generalized Gaussian, and Stable distributions. We confirm the presence of heteroscedasticity in the mapped matrix using the Engle hypothesis test, verifying ARCH/GARCH effects. The 2D MC-GARCH-M model, with its location-dependent conditional variances, provides a flexible approach for noise removal and modeling complex stochastic processes. Experimental results on artificially speckled aerial and actual SAR images demonstrate that our method outperforms state-of-the-art approaches in terms of speckle removal and preserving the edges and textural information of the image. Key innovations include: a new statistical analysis using linear mapping T showing non-Gaussian heavy-tailed distributions; an adaptive Bayesian estimator, MCMAP, based on the heteroscedastic model for speckle noise removal; and the method's applicability to images with varying mutual information (MI) between real and imaginary parts of 2D DOST coefficients. Our method is adaptive and robust to initial parameter settings, effectively utilizing magnitude and phase information, providing an optimal closed-form solution, reducing memory and computational requirements, and demonstrating robustness to speckle power levels. While requiring more computational resources compared to thresholding methods, our approach is less demanding than other Bayesian algorithms, making it suitable for offline processing.

Tri-wavelength blending method for speckle reduction and color space enhancement in laser projection systems

A novel tri-wavelength blending speckle reduction method was proposed and studied both experimentally and theoretically. In the proposed method, a 543 nm diode pumped solid state (DPSS) laser was employed to cooperated with a shorter wavelength 523 nm laser diode (LD) and a common 532 nm DPSS laser. Comparing to the traditional speckle reduction method, the proposed method takes into account both speckle reduction and color space coverage of a project system. It was shown that speckle contrast ratio (SCR) as low as 3.3 % was achieved when the power ratio of 523 nm, 532 nm and 543 nm light were set to 4:1:1. The effect on the color gamut after the use of tri-wavelength blending method was also investigated. It was found that by using the proposed tri-wavelength blending green lasers together with the commercially available red (635 nm) and blue (467 nm) laser diodes, a color space coverage as large as 97.6 % of the Rec.2020 color gamut can be achieved.

Real-time uncertainty reduction in laser triangulation via dynamic speckle correlation

The speckle-induced measurement uncertainty is one of the major bottlenecks to achieve high measurement accuracy in laser triangulation sensors (LTS). Conventional methods for improving uncertainty focus on suppressing speckles and refining centroid extraction algorithms, overlooking the potential information about object motion that speckles inherently contain. To address this deficiency, a dynamic speckle correlation (DSC) method is proposed to reduce speckle-induced measurement uncertainty in laser triangulation systems. This method uses the correlation between speckle patterns and the movement of the object being measured, thereby enabling real-time error compensation without any speckle reduction mechanisms. To verify the effectiveness of the DSC method, we have designed a compact LTS based on oblique incident laser. Extensive experiments demonstrate that, for displacement measurement of objects with surface roughness ranging from Ra0.4 to Ra3.2, the DSC method can effectively reduce the root mean square (RMS) measurement error to 3∼5μm. Compared with using the traditional grayscale centroid method alone, the measurement accuracy after DSC compensation is improved by at least 20%.

Two-Phase Speckle Noise Removal in US Images: Speckle Reducing Improved Anisotropic Diffusion and Optimal Bayes Threshold

Medial images are contaminated by multiplicative speckle noise, which dramatically reduces ultrasound images and has a detrimental impact on a variety of image interpretation tasks. Hence, to overcome this issue, this paper presented a Two-Phase Speckle Reduction approach with Improved Anisotropic Diffusion and Optimal Bayes Threshold termed TPSR-IADOT, which includes the phases like image enhancement and two-level decomposition processes. Initially, the speckle noise is subjected to an image enhancement process where the Speckle Reducing Improved Anisotropic Diffusion (SRAID) filtering process is carried out for the speckle removal process. Afterwards, two-level decomposition takes place which utilizes Discrete Wavelet Transform (DWT) to remove the residual noise. As the speckle noise is mostly present in the high-frequency band, Improved Bayes Threshold will be applied to the high- frequency subbands. Finally, to provide the best outcomes, an optimization algorithm termed Self Improved Pelican Optimization Algorithm (SI-POA) in this work via choosing the optimal threshold value. The efficiency of the proposed method has been validated on an ultrasound image database using Simulink in terms of PSNR, SSIM, SDME and MAPE. Accordingly, from the analysis, it is proved that the proposed TPSR-IADOT attains the PSNR of 40.074, whereas the POA is 38.572, COOT is 38.572, BES is 37.003, PRO is 30.419, WOA is 33.218, RFU-LA is 29.935 and SSI-COA is 39.256, for noise variance[Formula: see text]0.1.

Role of Renal Sonography in the Diagnosis of Chronic Kidney Disease

Aim: to use sonographic imaging in grading CKD and assess the serum creatinine, renal longitudinal size,parenchymal thickness and compare these parameters based upon ultrasonographic grade.Material and Methods: Present cross-sectional; study was conducted at the Department of Radiology, Tertiary Care Teaching Institute of India for the duration of 1 year. There are 120 participants with CKD in the research.All patients received a questionnaire, and a thorough clinical examination was done. Each participant underwent sector curved array transducer of 3.5–5 MHz ultrasound images of their liver and kidneys. Low tissue harmonic and speckle reduction imaging techniques are used to measure the echogenicity of the kidney and liver while reducing interobserver bias. In all the participants, the mean values of both the kidneys renal longitudinal size and parenchymal thickness were calculated. Renal cortical echogenicity was compared and graded with the echogenicity of the liver and renal medulla.Results: The mean age of the CKD patients was found to be 45.23 ± 06.05 years. It was found that 50 patientshad Grade 1, 40 patients had Grade 2, 20 patients had Grade 3 and 10 patients had Grade 4. The results showed that, when the comparison was done, serum creatinine was found to be significant among echogenicity grades with ANOVA. when the comparison was done; the mean longitudinal size was found to be significant among echogenicity. On comparison was found to be mean Parenchymal thickness was found to be significant among echogenicity grades.Conclusion: Renal cortical echogenicity and its grading in relation to longitudinal length, parenchymal thickness, and cortical thickness in CKD patients are the best sonographic parameters that correlate with blood creatinine. It is possible to use renal cortical echogenicity as a metric of renal function since it has the benefit of being irreversible in compared to serum creatinine levels.

4‐1: Invited Paper: Effective Speckle Reduction in Laser Projection Systems Using a Low Cost Screen with Multilayer Structure

4‐1: <i>Invited Paper:</i> Effective Speckle Reduction in Laser Projection Systems Using a Low Cost Screen with Multilayer Structure

Speckle reduction for single sideband-encoded computer-generated holograms by using an optimized carrier wave

Computer-generated hologram (CGH) is an evolving field that facilitates three-dimensional displays, with speckle noise reduction being a pivotal challenge. In hologram synthesis, complex data with random phase distributions are typically employed as carrier waves for wide viewing angles and a shallow depth of focus (DOF). However, these carrier waves are a source of speckle noise, which can significantly degrade image quality. In this paper, we propose a novel technique for speckle reduction for single sideband (SSB)-encoded holograms, applicable to any arbitrary 3D object. The proposed method focuses on optimizing the random carrier wave used in the hologram synthesis to achieve a uniform amplitude distribution at the object's location. This optimization results in a carrier wave that consistently exhibits uniform amplitude at specific depth planes, leading to a significant reduction of the speckle occurring from the carrier wave. The proposed method has been validated through simulations and optical experiments.

Design and development of speckle-free high-power laser-driven phosphor converted compact automotive headlamp module

The applicability of diode-lasers in automobile headlights is an advanced innovation for the automobile illumination industry due to the extraordinary properties of laser light over conventional light sources, such as high brightness, wide colour gamut, high directionality, low energy consumptions and long lifetime. Lasers are highly coherent in nature, so they encounter the problem of unwanted speckles and spurious fringes and always require a high level of opto-thermal engineering along with speckle reduction mechanisms for high lumen laser applications. Targeting such challenges, in this paper, we report an innovative design and development scheme for a high lumen laser-based automotive headlamp module. The headlamp prototype comprises a set of four cylindrical diffusers which distribute the high energy laser radiation via scattering along the length of the diffusers within a metallic mirro-based pyramidal cavity reflector. The scattered laser light from cylindrical diffusers interacts with a remote phosphor layer that prevents phosphor–resin burning. The pyramidal cavity reflector plays an important role in making the laser light uniform and speckle-free, via spatial and angular diversity, as light exits from the cavity after multiple internal reflections. This reflector redirects the highly concentrated white light over a long range without using any projection lens. The design and performance of the headlight system was studied using TracePro simulation software and tested experimentally in a photometric laboratory. The International Commission on Illumination (CIE) coordinates of the light generated by the headlamp was (0.3947, 0.4908) and the correlated colour temperature was 4240 K, which represents warm white light illumination.

Speckle Reduction in Digital Holography by Fast Logistic Adaptive Non-Local Means Filtering

Digital holography is a promising imaging technology. However, there is speckle noise in the reconstructed image of a digital hologram. Speckle degrades the quality of the reconstructed image. Suppression of speckle noise is a challenging problem in digital holography. A novel method is proposed to reduce speckle by a fast logistic adaptive non-local means (LA-NLM) algorithm. In the proposed method, the logistic function is incorporated into the weight calculation of the NLM algorithm to account for multiplicative speckle noise. Filtering parameters are dynamically adjusted according to the statistical property of speckle in the reconstructed image. To enhance computational efficiency, the proposed algorithm takes advantage of the integral image technique to speed up the calculation of the similarity between image patches. Simulated and experimental digital holograms are obtained to verify the proposed method. The results show that the speckle noise is effectively suppressed in digital holography. The proposed method is efficient and feasible, and can be applied to such fields as three-dimensional display, holographic measurement, and medical diagnosis.

Self-supervised Self2Self denoising strategy for OCT speckle reduction with a single noisy image.

Optical coherence tomography (OCT) inevitably suffers from the influence of speckles originating from multiple scattered photons owing to its low-coherence interferometry property. Although various deep learning schemes have been proposed for OCT despeckling, they typically suffer from the requirement for ground-truth images, which are difficult to collect in clinical practice. To alleviate the influences of speckles without requiring ground-truth images, this paper presents a self-supervised deep learning scheme, namely, Self2Self strategy (S2Snet), for OCT despeckling using a single noisy image. Specifically, in this study, the main deep learning architecture is the Self2Self network, with its partial convolution being updated with a gated convolution layer. Specifically, both the input images and their Bernoulli sampling instances are adopted as network input first, and then, a devised loss function is integrated into the network to remove the background noise. Finally, the denoised output is estimated using the average of multiple predicted outputs. Experiments with various OCT datasets are conducted to verify the effectiveness of the proposed S2Snet scheme. Results compared with those of the existing methods demonstrate that S2Snet not only outperforms those existing self-supervised deep learning methods but also achieves better performances than those non-deep learning ones in different cases. Specifically, S2Snet achieves an improvement of 3.41% and 2.37% for PSNR and SSIM, respectively, as compared to the original Self2Self network, while such improvements become 19.9% and 22.7% as compared with the well-known non-deep learning NWSR method.