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Non-local means (NLM) method is a powerful technique in the field of image processing. The center weight CW (the weight of the pixel to be denoised) plays an important role for the performance of NLM. In this paper, several center weights such as Zero-CW and One-CW are researched in the influence of these weights on denoising performance. In order to avoid the disadvantages of excessive smoothness or insufficient denoising of these different NLM filters, a two-step non-local means (TSNLM) iterative scheme is proposed. In the first step, local Wiener filter is introduced to extract image features from the method noise of NLM with Zero-CW. The denoising process is integrated into NLM based on local Wiener filter (LWF-NLM). In the second step, the carefully selected NLM (NLM with One-CW) operates on the output of the first step to remove the remaining noise. The denoising amount of two steps is combined by the decaying parameter depending on noise variance. As far as I know, this is the first time to consider the role of center weight to design an iterative NLM filter. The experimental results show that the proposed TSNLM helps NLM to improve the ability of denoising, giving satisfactory subjective and objective performance. Furthermore, the proposed TSNLM is very efficient compared to other related NLM based iterative methods.

Many methods have been proposed for image denoising, among which the non-local means (NLM) denoising is widely used for fully exploiting the self-similarity of natural images. For NLM denoising, it needs to calculate the similarity of clean image blocks as weights. But affected by noise, it is challenging to accurately get the similarity of clean image patches. Most existing NLM denoising approaches often cause the restored image to be over smoothed and lose lots of details, especially for image with high noise levels. To tackle this, a novel singular value decomposition-based similarity measure method is proposed, which can effectively reduce the disturbance of noise. For the method, we first calculate and vectorize the singular values of two image patches extracted from the noisy image and compute Euclidean distances and cross-angles of the vectors. Second, we propose to utilize the geometric average of Euclidean distance and cross-angle to calculate the similarity between two image patches which is resistant to noise. Third, the proposed similarity measure is applied to non-local means denoising to compute similarity of noisy image patches. Experimental results show that compared to state-of-the-art denoising algorithms, the proposed method can effectively eliminate noise and restore more details with higher peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM) index values.

It is necessary to conserve important information, like edges, details, and textures, in CT aortic dissection images, as this helps the radiologist examine and diagnose the disease. Hence, a less noisy image is required to support medical experts in performing better diagnoses. In this work, the non-local means (NLM) method is conducted to minimize the noise in CT images of aortic dissection patients as a preprocessing step to produce accurate aortic segmentation results. The method is implemented in an existing segmentation system using six different kernel functions, and the evaluation is done by assessing DSC, precision, and recall of segmentation results. Furthermore, the visual quality of denoised images is also taken into account to be determined. Besides, a comparative analysis between NLM and other denoising methods is done in this experiment. The results showed that NLM yields encouraging segmentation results, even though the visualization of denoised images is unacceptable. Applying the NLM algorithm with the flat function provides the highest DSC, precision, and recall values of 0.937101, 0.954835, and 0.920517 consecutively.

Objectives Non-local mean (NLM) filtering has been broadly used for denoising of natural and medical images. The NLM filter relies on the redundant information, in the form of repeated patterns/textures, in the target image to discriminate the underlying structures/signals from noise. In PET (or SPECT) imaging, the raw data could be reconstructed using different parameters and settings, leading to different representations of the target image, which contain highly similar structures/signals to the target image contaminated with different noise levels (or properties). In this light, multiple-reconstruction NLM filtering (MR-NLM) is proposed, which relies on the redundant information provided by the different reconstructions of the same PET data (referred to as auxiliary images) to conduct the denoising process. Methods Implementation of the MR-NLM approach involved the use of twelve auxiliary PET images (in addition to the target image) reconstructed using the same iterative reconstruction algorithm with different numbers of iterations and subsets. For each target voxel, the patches of voxels at the same location are extracted from the auxiliary PET images based on which the NLM denoising process is conducted. Through this, the exhaustive search scheme performed in the conventional NLM method to find similar patches of voxels is bypassed. The performance evaluation of the MR-NLM filter was carried out against the conventional NLM, Gaussian and bilateral post-reconstruction approaches using the experimental Jaszczak phantom and 25 whole-body PET/CT clinical studies. Results The signal-to-noise ratio (SNR) in the experimental Jaszczak phantom study improved from 25.1 when using Gaussian filtering to 27.9 and 28.8 when the conventional NLM and MR-NLM methods were applied (p value p value p value Conclusion The MR-NLM approach exhibited promising performance in terms of noise suppression and signal preservation for PET images, thus translating into higher SNR compared to the conventional NLM approach. Despite the promising performance of the MR-NLM approach, the additional computational burden owing to the requirement of multiple PET reconstruction still needs to be addressed.

MRI images suffer from a type of multiplicative noise called Rician noise. The traditional smoothing filters are not effective to reduce this type of noise. Non local Means (NLM) filters with bias correction is a promising technique for such type of signal dependent noise. The NLM denoising is based on self-similarity and is computationally very expensive. Many researchers are working to improve its time complexity. In most cases there is a tradeoff between speed and quality of denoised image. This paper proposes a speed enhanced NLM method that use integral image and fuzzy Jacord similarity measure to find the self-similarity of sub windows. This helps to find the weights of similar pixel at a faster rate than the traditional NLM algorithm and more accurately than the existing fast NLM method. Consequently, these similar pixels are used to generate the noise free pixels. At the end the conventional bias subtraction method is used as post processing step. The proposed scheme is tested with the standard brain web data set with different noise levels and compared with existing Fast NLM techniques and basic NLM using Root mean square error (RMSE), peak signal noise ratio (PSNR), Structure similar index (SSIM), quality index and computational time parameters methods. The proposed method gives better result than existing Fast NLM technique with high density Rician noise in the image and is 20 times faster than traditional NLM.

Magnetic resonance (MR) images suffer from noise introduced by various sources. Due to this noise, diagnosis remains inaccurate. Thus, removal of noise becomes a very important task when dealing with MR images. In this paper, a denoising method has been discussed that makes use of non-local means filter and discrete total variation method. The proposed approach has been compared with other noise removal techniques like non-local means filter, anisotropic diffusion, total variation, and discrete total variation method, and it proves to be effective in reducing noise. The performance of various denoising methods is compared on basis of metrics such as peak signal-to-noise ratio (PSNR), mean square error (MSE), universal image quality index (UQI), and structure similarity index (SSIM) values. This method has been tested for various noise levels, and it outperformed other existing noise removal techniques, without blurring the image.

Depth maps are used to describe object positioning information in three-dimensional (3D) space, and they are crucial for RGB-D data representation, which is useful for numerous interactive visual applications. In practice, depth maps are often contaminated by compound noise, including intrinsic noise and missing regions owing to active illumination shadows. As existing noise models cannot describe the above-mentioned compound noise effectively, the subsequent filter design is a challenging task. In this study, an adaptive colour-guided non-local mean (NLM) filter is proposed to address such compound noise. First, the authors classify the depth map into hole and non-hole pixels. Then, the proposed filter is designed on the basis of the NLM framework, where the colour image is used as a guide prior for hole-artifact removal. Finally, the authors use a shock filter to effectively address the non-regularisation of the restored depth map edges and remove the remaining noise. Experiments show that the proposed filter qualitatively and quantitatively outperforms existing colour-guided and unguided filters. Moreover, the authors verify the superiority of the proposed filter through virtual view synthesis and 3D scene reconstruction applications.

The diagnosis of dementia, particularly in the early stages is very much helpful with Positron emission tomography (PET) image processing. The most important challenges in PET image processing are noise removal and region of interests (ROIs) segmentation. Although denoising and segmentation are performed independently, but the performance of the denoising process significantly affects the performance of the segmentation process. Due to the low signals to noise ratio and low contrast, PET image denoising is a challenging task. Individual wavelet, curvelet and non-local means (NLM) based methods are not well suited to handle both isotropic (smooth details) and anisotropic (edges and curves) features due to its restricted abilities. To address these issues, the present work proposes an efficient denoising framework for reducing the noise level of brain PET images based on the combination of multi-scale transform (wavelet and curvelet) and tree clustering non-local means (TNLM). The main objective of the proposed method is to extract the isotropic features from a noisy smooth PET image using tree clustering based non-local means (TNLM). Then curvelet-based denoising is applied to the residual image to extract the anisotropic features such as edges and curves. Finally, the extracted anisotropic features are inserted back into the isotropic features to obtain an estimated denoised image. Simulated phantom and clinical PET datasets have been used in this proposed work for testing and measuring the performance in the medical applications, such as gray matter segmentation and precise tumor region identification without any interaction with other structural images like MRI or CT. The results in the experimental section show that the proposed denoising method has obtained better performance than existing wavelet, curvelet, wavelet-curvelet, non-local means (NLM) and deep learning methods based on the preservation of the edges. Qualitatively, a notable gain is achieved in the proposed denoised PET images in terms of contrast enhancement than other existing denoising methods.

Local filters used for denoising images do not account for nonlinear structure within the image, where similarities can be shared between spatially distant pixels which are overcome by nonlocal filters by allowing for the use of self-predictions . In this method, an improved version of nonlocal denoising filtering has been presented using quaternion non coupled means filter. The proposed method has been applied to various noisy images and shows good results in terms of performance characteristics.

Image super-resolution (SR) techniques aim to estimate high-resolution (HR) image from low-resolution (LR) image. Existing SR method has slow convergence and recovery of high-frequency details are ...

Objective The survival rates of breast cancer are increasing as screening and diagnosis improve. The removal of noise is revealed to be a significant step for automatic - computer aided detection (CAD) of microcalcification in digital mammography. Methods In this paper, a combined approach for eradicating impulse noise from digital mammograms is proposed. The process is achieved in two stages, detection of noise followed by filtering of noise. The detection of noise is carried out by using Modified Robust Outlyingness Ratio (mROR) trailed by an extended NL (Non-Local)-means filter for filtering mechanism. Results According to the value of mROR, all pixels in mammogram images are divided into four distinct groups. In each cluster, many decision rules are then applied for detecting the impulse noise. Filtering is done with NL-means filter by providing a reference mammogram image. Conclusion The comparative analysis and evaluated results are compared with some existing filters which indicate that the proposed structure outperforms the analysed result of others.

Background The Rician noise formed in magnetic resonance (MR) imaging greatly reduced the accuracy and reliability of subsequent analysis, and most of the existing denoising methods are suitable for Gaussian noise rather than Rician noise. Aiming to solve this problem, we proposed fuzzy c-means and adaptive non-local means (FANLM), which combined the adaptive non-local means (NLM) with fuzzy c-means (FCM), as a novel method to reduce noise in the study. Method The algorithm chose the optimal size of search window automatically based on the noise variance which was estimated by the improved estimator of the median absolute deviation (MAD) for Rician noise. Meanwhile, it solved the problem that the traditional NLM algorithm had to use a fixed size of search window. Considering the distribution characteristics for each pixel, we designed three types of search window sizes as large, medium and small instead of using a fixed size. In addition, the combination with the FCM algorithm helped to achieve better denoising effect since the improved the FCM algorithm divided the membership degrees of images and introduced the morphological reconstruction to preserve the image details. Results The experimental results showed that the proposed algorithm (FANLM) can effectively remove the noise. Moreover, it had the highest peak signal-noise ratio (PSNR) and structural similarity (SSIM), compared with other three methods: non-local means (NLM), linear minimum mean square error (LMMSE) and undecimated wavelet transform (UWT). Using the FANLM method, the image details can be well preserved with the noise being mostly removed. Conclusion Compared with the traditional denoising methods, the experimental results showed that the proposed approach effectively suppressed the noise and the edge details were well retained. However, the FANLM method took an average of 13 s throughout the experiment, and its computational cost was not the shortest. Addressing these can be part of our future research.

Electrocardiogram, popularly known as ECG is a diagnostic test which detects the heart’s electrical system. It is an inevitable diagnostic technique in the medical field which helps to detect any heart related diseases accurately. But there are high chances of addition of any random unwanted signals or noises during the recording process. This hinders the exact diagnosis and hence it is important to denoise these signals. There exist various denoising techniques including Empirical mode decomposition (EMD), Non local means (NLM), various filters, etc. A modified method of EMD is presented here which performs smaller number of iterations and forms only a few Intrinsic mode functions (IMF) when compared with traditional EMD method. The presented work is a performance comparison study between the modified version of EMD which has lesser latency with the traditional EMD.

The topic of denoising magnetic resonance (MR) images is considered in this paper. More in detail, an enhanced Non-Local Means (NLM) filter using the Kolmogorov-Smirnov (KS) distance is proposed. The KS-NLM approach estimates the similarity between image patches by computing the KS distance. To overcome that NLM filters assign the same role to all pixels in patches, that is, not privileging the central one, we propose a new filter, namely the Anisotropic Weighted KS-NLM (Aw KS-NLM), which better deals with central pixels within the patches by, on one hand, including a suitable weighted strategy and, on the other, by performing a local anisotropy analysis. The Aw KS-NLM has been compared to other existing non-local Means (NLM) methodologies in both MRI simulated and real datasets. The results provide excellent noise reduction and image-detail preservation.

In real industry environment, the signal characteristics of multistage centrifugal pump vibration signal are easily submerged by strong background noise. To settle this problem, the nonlocal means (NLM) approach is proposed for the denoising of multistage centrifugal pump in this paper. Utilizing the similarity theory, the NLM method has achieved a wide range of applications in the fields of image processing and biomedical signal denoising. Due to the periodic characteristics and redundancy, NLM is successfully applied to the de-noising of 1-D machinery vibration signal. The numerical simulation experiments with different SNRs verify the effectiveness and the superiority of the proposed method. Besides, the selection principles of core parameters in NLM are discussed. The real engineering cases analysis demonstrates that the NLM can effectively filter out the background noise and realize the weak fault feature enhancements. The proposed noise reduction method is superior to traditional wavelet coefficient method.

Diffusion magnetic resonance imaging (DMRI) suffers from lower signal-to-noise-ratio (SNR) due to MR signal attenuation associated with the motion of water molecules. To improve SNR, the non-local means (NLM) algorithm has demonstrated state-of-the-art performance in noise reduction. However, existing NLM algorithms do not take into account explicitly the fact that DMRI signal can vary significantly with local fiber orientations. Applying NLM naively can hence blur subtle structures and aggravate partial volume effects. To overcome this limitation, we improve NLM by performing neighborhood matching in non-flat domains and removing noise with information from both ${x}$ -space (spatial domain) and ${q}$ -space (wavevector domain). Specifically, we first encode the ${q}$ -space sampling domain using a graph. We then perform graph framelet transforms to extract robust rotation-invariant features for each sampling point in x-q space. The resulting features are employed for robust neighborhood matching to locate recurrent information. Finally, we remove noise via an NLM framework. To adapt to the various types of noise in multi-coil MR imaging, we transform the signal before denoising so that it is Gaussian-distributed, allowing noise removal to be carried out in an unbiased manner. Our method is able to more effectively locate recurrent information in white matter structures with different orientations, avoiding the blurring effects caused by naively applying NLM. Experiments on synthetic, repetitively-acquired, and infant DMRI data demonstrate that our method is able to preserve subtle structures while effectively removing noise.

A confined, non-invasive and accurate diagnosis procedure is preferred for investigation of numerous human body disorders. Magnetic Resonance (MR) Imaging is one such crucial and popular diagnostic procedure and therefore these MR images need to be noise free to reduce the chances of error in diagnosis. An improved and novel approach has been suggested to denoise MR images by utilizing unbiased non local means (UNLM) method and anisotropic diffusion method. The proposed method's performance has been compared with other denoising techniques and the results were evaluated on metrices like Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and Structure Similarity Index (SSIM) values. The proposed method produced successful results and the process of anisotropic diffusion was hence improved. The MR images were well denoised and fine structures were also preserved along with the edges.

This paper presents a non-local means low-rank approximation (NLMLRA) denoising method for hyperspectral images (HSIs). NLMLRA uses a Slanted Butterworth function to construct a low-rank approximation for non-local means (NLM) operator and is efficiently implemented based on Chebyshev polynomials. The proposed method is evaluated by using both simulated and real hyperspectral datasets.

The Non-Local Means (NLM) algorithm has gained wide applications in image denoising. However, the effect in practical application is limited by parameter setting and the pre-defined filtering parameter has to manually set in different noise levels. To solve the problem, we analyze the effect of the filtering parameter on images with different texture complexity and noise levels. Then, a new NLM algorithm is proposed based on the eigenvalues of the pixel to adjust the filtering parameter adaptively. Specifically, the linear scale space theory is used to obtain the Hessian matrix of each pixel in the image and calculate the associated eigenvalues, based on which a formula is developed to determine the filtering parameter. In order to achieve edge-preserving performance, the filtering parameter is selected differently. Experimental results show that filtering parameter adaptively determined by image noise levels as well as image texture, and accordingly achieve excellent denoising performance.

Non-local mean (NLM) adaptive filtering is a well-explored technique for the denoising of images and electrocardiogram signals. In NLM filtering, the signal value at a particular sample point is estimated by a weighted average of sample points over a search neighborhood. The NLM filter effectively removes the noise when there are similarities among the samples of the signal over the search neighborhood. Due to the time-varying nature of the vocal-tract system and excitation source, the magnitude and frequency of the speech signal vary over the time. Consequently, NLM filtering is not effective in removing the noise components from the speech signal. The similarity among the sample points can be improved by classifying the speech signal into different categories depending on the magnitude and frequency components. In a given speech signal, the vowel-like speech (VLS) are high-magnitude regions compared to the other non-VLS. The vowel, semivowel and diphthong sound units are collectively termed as VLS. In this work, at the first level, the noisy speech signal is classified into VLS and non-VLS for improving similarity. Next, the non-local similarity present within the VLS and the non-VLS is exploited separately for an effective speech enhancement through NLM filtering. The experimental results presented in this study show that the proposed approach provides better denoising performance when compared with the NLM filtering without speech classification as well as recently reported speech enhancement methods. The hardware architecture of the proposed approach is also designed and prototyped on FPGA.

Gamma-ray images generally suffer from a lot of noise because of low photon detection in the gamma camera system. The purpose of this study is to improve the image quality in gamma-ray images using a gamma camera system with a fast nonlocal means (FNLM) noise reduction algorithm with an acceleration function. The designed FNLM algorithm is based on local region considerations, including the Euclidean distance in the gamma-ray image and use of the encoded information. To evaluate the noise characteristics, the normalized noise power spectrum (NNPS), contrast-to-noise ratio (CNR), and coefficient of variation (COV) were used. According to the NNPS result, the lowest values can be obtained using the FNLM noise reduction algorithm. In addition, when the conventional methods and the FNLM noise reduction algorithm were compared, the average CNR and COV using the proposed algorithm were approximately 2.23 and 7.95 times better than those of the noisy image, respectively. In particular, the image-processing time of the FNLM noise reduction algorithm can achieve the fastest time compared with conventional noise reduction methods. The results of the image qualities related to noise characteristics demonstrated the superiority of the proposed FNLM noise reduction algorithm in a gamma camera system. Keywords: Medical application, Gamma camera system, Noise reduction algorithm, Total variation noise reduction method, Evaluation of image quality

We propose and experimentally demonstrate an adaptive image restoration method based on constrained least squares filtering (CLS) and noise level estimation by principal component analysis of low-rank patches (PCA) that enhances the signal-to-noise ratio (SNR) of Brillouin optical time domain analysis sensor (BOTDA). The proposed method employs PCA to estimate the initial noise level of the sensing image and subsequently the input parameter for CLS can be adjusted by a constraint factor. Furthermore, to effectively remove the noise in the raw data, we introduce a calculation procedure for which the noise level of the denoising image serves as a criterion. This procedure is adaptive and can restore the optimal Brillouin gain spectrum. In the experiment, the proposed method shows an increase of 12.2 dB in SNR and that the temperature accuracy can be increased to 1.3 °C. The adaptive CLS is also applied to sensing data with the different noise level, which validates that this method is a valid denoising technique. This report describes that image restoration, including non-local means, wavelet filter and CLS, has been applied to conventional BOTDA with the spatial resolution of 1m. Due to the influence of linewidth broadening for a 10 ns pulse, these methods all show sub-meter distortion in resolution, whereas the proposed method has the least impact on the spatial resolution with 1.41m. The proposed method is adaptive and can provide faster processing speed; thus it can be considered to be an alternative technique to enhance the performance of distributed optical fiber sensors, especially for long-distance and high-resolution sensing.

Magnetic resonance imaging (MRI) has many advantages and has developed various pulse sequences. In particular, the diffusion weighted image (DWI) technique is widely used because it can acquire images quickly during examination of stroke, through a proper adjustment of the diffusion-weighted gradient b-value. However, a setting with inappropriate b-value causes loss of image signal that increases the influence of noise. Therefore, in this study, we quantitatively evaluated image quality after applying a variety of algorithms to the image acquired by changing the b-value and the main magnetic field in the MRI device. To acquire the image, the phantom was self-produced with an acrylic panel and chicken breast. Wiener filter, total variation (TV), and our proposed fast non-local means (FNLM) noise reduction algorithms were applied to the image. Consequently, the signal intensity at a 3.0 T magnetic field increased by a factor 4.8 compared to a 1.5 T magnetic field. Moreover, the signal-to-noise ratio and contrast-to-noise ratio were highest with the FNLM algorithm, and the values increased by factors of 9.5 and 9.9 with a 1.5 T magnetic field and by factors of 9.9 and 5.0 with a 3.0 T magnetic field compared to the noise image, respectively. The result of time resolution, the Wiener filter appeared the finest value, but had no significant difference compared to FNLM algorithm. In conclusion, our results confirmed that the proposed FNLM noise reduction algorithm can acquire both improved image quality and high processing time in MRI imaging with the DWI technique.

Diffusion MRI affords valuable insights into white matter microstructures, but suffers from low signal-to-noise ratio (SNR), especially at high diffusion weighting (i.e., b-value). To avoid time-intensive repeated acquisition, post-processing algorithms are often used to reduce noise. Among existing methods, non-local means (NLM) has been shown to be particularly effective. However, most NLM algorithms for diffusion MRI focus on patch matching in the spatial domain (i.e., x-space) and disregard the fact that the data live in a combined 6D space covering both spatial domain and diffusion wavevector domain (i.e., q-space). This drawback leads to inaccurate patch matching in curved white matter structures and hence the inability to effectively use recurrent information for noise reduction. The goal of this paper is to overcome this limitation by extending NLM to the joint x-q space. Specifically, we define for each point in the x-q space a spherical patch from which we extract rotation-invariant features for patch matching. The ability to perform patch matching across q-samples allows patches from differentially orientated structures to be used for effective noise removal. Extensive experiments on synthetic, repeated acquisition, and real data demonstrate that our method outperforms state-of-the-art methods, both qualitatively and quantitatively., Comment: 36 pages, 14 figures

Denoising plays a crucial role in the field of medical imaging in regard to the improvement of image quality. In this study, a fast nonlocal means (FNLM) denoising algorithm which utilizes neighborhood filtering is proposed and implemented for early breast cancer detection based on medical mammography. For comparison with conventional denoising methods, the Wiener filter and total variation (TV) denoising algorithm were used. The temporal resolution, coefficient of variation (COV), and contrast to noise ratio (CNR) were evaluated for three exposure conditions: (a) various tube voltages at fixed 40 mAs, (b) various tube currents at fixed 28 kVp, and (c) auto exposure control mode. The results showed that the proposed FNLM denoising algorithm can achieve a similar temporal resolution to the Wiener filter and may efficiently reduce image noise by using COV and CNR values in mammography.

In chest radiography, a solitary pulmonary nodule, which may be a precursor of lung cancer, is a frequently detected finding. However, as the image quality is deteriorated owing to the increase in the noise, lung cancer screening studies revealed that the likelihood of finding a nodule is lower than those of other modalities. This study quantitatively evaluates three widely used filters (median, Wiener, and total variation) and a newly proposed filter (fast non-local means (FNLM)), which reduce image noise. Images of a phantom with lung nodules, obtained from a patient using the 3D printing technology, were acquired at the chest anterior–posterior, lateral, and posterior–anterior positions. To evaluate their denoising performance, normalized noise power spectrum, contrast to noise ratio and coefficient of variation were used. In the quantitative evaluation of the overall image, the proposed FNLM filter exhibited the best image performance. In the quantitative evaluation of the nodule image, the FNLM filter, which exhibits outstanding denoising performance and time efficiency, can be employed. Therefore, with the use of the FNLM filter in chest radiography, the detection probability of a nodule, which can be a precursor of lung cancer, is increased, and the cancer can be prevented even with a lower dose.

The noise reduction algorithms have been widely used in the field of medical X-ray imaging system using semiconductor photon counting detector (PCD). Among these algorithms, fast non-local means (FNLM) method is based on use of the encoded information and considering the local region in the image. The aim of this study was to design FNLM noise reduction algorithm using acceleration function with Euclidean distance, which is feasible to cope with practical problems, and to confirm its application feasibility in CdTe PCD. We simulated X-ray imaging system with PCD using Monte Carlo simulation tool (Geant4 Application for Tomographic Emission (GATE)) version 6 and applied conventional and proposed noise reduction algorithms in acquired water phantom image. We conclude that the results of the image performances associated with noise characteristics demonstrated the superiority of the proposed FNLM noise reduction algorithm in CdTe PCD imaging system.

Integration of signal average and image restoration can improve the performance of distributed optical fiber sensors. Here we illustrate the denoising behavior of non-local means filtering (NLM) affected by number of average. On the one hand, the signal average can effectively remove the high-frequency noise but be time-consuming; on the other hand, the NLM can accelerate decent in noise but is dependent on level of noise contained in raw sensing image. To best of our knowledge, this is the first time to apply NLM to conventional Brillouin optical time domain analysis sensor (BOTDA) with theoretically spatial resolution of 10 ns. Moreover, we propose an optimal averaging number determination method for further denoising by NLM to achieve trade-off between accuracy and average times. The results indicated that the optimal-times for BOTDA configured 10 ns and 10km fiber-loop is 13 with Brillouin frequency shift uncertainty of 0.85MHz. The proposed method has potential to apply in engineering applications and commercialized software for BOTDA denoising by image restoration.

The purpose of this study is to evaluate the various control parameters of a modeled fast non-local means (FNLM) noise reduction algorithm which can separate color channels in light microscopy (LM) images. To achieve this objective, the tendency of image characteristics with changes in parameters, such as smoothing factors and kernel and search window sizes for the FNLM algorithm, was analyzed. To quantitatively assess image characteristics, the coefficient of variation (COV), blind/referenceless image spatial quality evaluator (BRISQUE), and natural image quality evaluator (NIQE) were employed. When high smoothing factors and large search window sizes were applied, excellent COV and unsatisfactory BRISQUE and NIQE results were obtained. In addition, all three evaluation parameters improved as the kernel size increased. However, the kernel and search window sizes of the FNLM algorithm were shown to be dependent on the image processing time (time resolution). In conclusion, this work has demonstrated that the FNLM algorithm can effectively reduce noise in LM images, and parameter optimization is important to achieve the algorithm’s appropriate application.

Raman scattering-based distributed sensor is nowadays the most mature and consolidated technology for distributed temperature sensing at long ranges. Even though the various applied methods in recent decades have made important improvements in performance of Raman sensors, they do not thoroughly exploit the high level of correlation and similitude existing in the acquired data. In the present, we exploit a 2D image processing method called non-local means (NLM) algorithm that takes full advantage of, compared to existing methods, highly redundant texture of the 1D data obtained from distributed Raman sensors. One of the main drawbacks of the original NLM filter in distributed sensors area is to establish complete squared similarity windows for denoising the boundary pixels, corresponding to the data placed at the last row of the data matrices, which constrains the best attainable SNR values. To overcome this issue, we introduce an unprecedented approach called partial window (PW) by which we utilize partial similarity windows for denoising the boundary pixels. The PW-based NLM (PW-NLM) technique not only leads to higher levels of SNR compared with the original one, but also has no detrimental effects on the denoised data. The simulation results demonstrate the SNR improvement of 2.8 dB using the PW-based NLM filter over the original NLM filter corresponding to the 17 dB SNR boost compared with the unprocessed data.

When a molecular dynamics (MD) simulation and a computational fluid dynamics (CFD) solver are coupled together to create a multiscale, molecular-continuum flow simulation, thermal noise fluctuations from the particle system can be a critical issue, so that noise filtering methods are required. Noise filters are one option to significantly reduce these fluctuations.

Non-local means (NLM) filter based on similarity structure index (SSIM) has the disadvantages of large amount of computation and long operation time. This paper proposes a method of NLM filter based on histogram to improve similarity measurement of similarity windows. By comparative experiments, this method can greatly shorten the time while keeping the filtering effect approximately unchanged, compared with the improved NLM filter based on SSIM.

Photoacoustic/Optoacoustic tomography aims to reconstruct maps of the initial pressure rise induced by the absorption of light pulses in tissue. This reconstruction is an ill-conditioned and under-determined problem, when the data acquisition protocol involves limited detection positions. The aim of the work is to develop an inversion method which integrates denoising procedure within the iterative model-based reconstruction to improve quantitative performance of optoacoustic imaging. Among the model-based schemes, total-variation (TV) constrained reconstruction scheme is a popular approach. In this work, a two-step approach was proposed for improving the TV constrained optoacoustic inversion by adding a non-local means based filtering step within each TV iteration. Compared to TV-based reconstruction, inclusion of this non-local means step resulted in signal-to-noise ratio improvement of 2.5 dB in the reconstructed optoacoustic images. Accepted version Phaneendra K. Yalavarthy acknowledges the DST-ICPS cluster funding (T-851) for the data science program. Part of the work is supported through IFTAS-CDS Collaborative Laboratory of Data Science and Engineering. Jaya Prakash acknowledges the IISc Startup Grant as well as DBT-IYBA Program.

Electroencephalogram EEG is designed for monitoring the electrical activity of the brain. Noisy EEG signals can cause errors in their diagnoses or analysis. Additive White Gaussian Noise (AWGN) is added to EEG signals as extrinsic noise. Electromyogram (EMG) signals are added to signals of EEG as intrinsic noise. Then noises are removed using modified non local means filter. Removing Noise is compared with filter of wavelet method or filter of nonlocal means filter (NLM) also. The performance obtained from modified NLM filter is excellent in removing both AWGN and EMG signals from EEG signals. Output signal to noise ratio (oSNR) in modified NLM filter outperforms oSNR in NLM filter with 5.37dB for removing AWGN. OSNR in modified NLM filter outperforms oSNR in filter of wavelet method with 0.39 dB for removing AWGN. OSNR in modified NLM filter outperforms oSNR in NLM filter with 3.75 but for removing EMG noise. OSNR in modified NLM filter outperforms oSNR in filter of wavelet method with 2.39dB for removing EMG noise.

The non-local means (NLM) noise reduction algorithm is well known as an excellent technique for removing noise from a magnetic resonance (MR) image to improve the diagnostic accuracy. In this study, we undertook a systematic review to determine the effectiveness of the NLM noise reduction algorithm in MR imaging. A systematic literature search was conducted of three databases of publications dating from January 2000 to March 2020, of the 82 publications reviewed, 25 were included in this study. The subjects were categorized into four major frameworks and analyzed for each research result. Research in NLM noise reduction for MR images has been increasing worldwide, however, it was found to have slightly decreased since 2016. It was found that the NLM technique was most frequently used on brain images taken using the general MR imaging technique, these were most frequently performed during simultaneous real and simulated experimental studies. In particular, comparison parameters were frequently used to evaluate the effectiveness of the algorithm on MR images. The ultimate goal is to provide an accurate method for the diagnosis of disease, and our conclusion is that the NLM noise reduction algorithm is a promising method of achieving this goal.