Noise Removal Algorithm Research Articles

Adaptive nonlinear filtering algorithms for removal of non-stationary noise in electronystagmographic signals

Non-stationary physiological noise poses significant difficulties due to its time-varying and previously unknown characteristics. When processing electronystagmographic signals, linear filtering can distort diagnostically significant rapid changes caused by saccadic eye movements. In such cases, nonlinear filters based on robust estimators are more appropriate, whereas linear filtering effectively reduces white noise in parts of a signal with linear behaviour. Therefore, an adaptive signal processing approach that varies in nonlinearity from highly nonlinear robust estimation to linear averaging and combines their benefits appears promising. We have proposed a low-complexity adaptive method for switching filter sets and relevant filter parameter settings based on the estimated noise level and local signal behaviour. This method does not require time for filter parameter modification and does not need prior information on the signal model and noise variance. Based on this method, we have designed adaptive nonlinear filtering algorithms to exploit the advantages of both the nonlinear robust and linear averaging estimators. We have evaluated the filtering quality statistically using the minimum mean square error criteria and the maximum signal-to-noise ratio for a model signal with different levels of additive Gaussian noise. The proposed real-time adaptive filtering algorithms demonstrate a significant efficiency improvement compared to the commonly used filters. The proposed adaptive myriad algorithm achieves the highest efficiency by adjusting a sample myriad linearity parameter depending on the local estimate of a signal scale and changing the sliding window length and the coefficient affecting the linearity parameter.

Delayed-X Harmonics Synthesizer algorithm for low frequency noise removal

As various technologies on noise reduction have improved their performances, slight low frequency noise has become more prominent in quiet environments. Since the traveling of low noise is difficult to be isolated by walls and earplugs, we aim an active noise control embedded in unoccluded headphones. In this case, both desired sounds (e.g., speech) and unwanted noises pass through the opened ears, so the algorithm is required to adapt only the noise separating from the speech (selective cancelation). Therefore, our selected procedure was Delayed-X Harmonics Synthesizer algorithm which produces pure tones in the opposite phase, because the generated pure tones in the low frequency can minimize the damage for the speech quality. To select the frequencies of pure tones, we focused on the fact that noise is more stationary than speech. The targets frequencies were determined by a small frame-variation on the spectra of the error signals. Our simulations demonstrated that the low noise (< 100 Hz) could be reduced by 10dB even in the slight noise condition (SNR = 5dB), while the speech intelligibility (PESQ) was maintained. When our system is embedded in the unoccluded headphones, the users will take daily conversation without any troubles of low noise.

Stripe Noise Removal Algorithm for Infrared Remote Sensing Images Based on Adaptive Weighted Variable Order Model

In the infrared remote sensing imaging system, the output of the detector is uneven, resulting in noticeable stripe noise in the image, which significantly reduces the image quality. Therefore, in response to the problem of establishing a variational model for the image globally and fixing the fractional order of the regularization term in the existing stripe noise removal algorithms based on model optimization, a stripe noise removal algorithm for infrared remote sensing images based on an adaptive weighted variable order model is proposed. In this algorithm, the vertical and approximate components containing stripe noise in the image are first separated through multi-level and multi-scale wavelet transform, and only these two components are processed; secondly, the global sparsity and the gradient sparsity of stripe noise and the variable order gradient sparsity of the information component are constrained by L1 norm, so as to establish the stripe noise removal model. The order of the fractional derivative is adaptively assigned to each pixel of the information component through local variance; then, an adaptive weight operator is introduced, which can assign different weights to the pixels in the variable order derivative of the information component on the basis of the image gradient information. Finally, through the ADMM algorithm, we can obtain the optimal solution of this model. The processing results of simulated and actual data indicate that the proposed algorithm performs well in all indexes and has obvious advantages in removing stripe noise and preserving image details.

Segmentation of MHD modes using Fourier transform, wavelets and computer vision algorithms

Abstract Magnetohydrodynamic (MHD) activity in fusion devices is typically analyzed by examining time-frequency spectrograms obtained from various diagnostics. MHD modes often co-exist with various types of noise and complex patterns generated by other events like pellet injection or active diagnostics. Traditionally, identifying MHD modes has been a manual task, making it labor-intensive. To overcome this issue, this study proposes the use of computer vision (CV) algorithms for noise removal and automatic feature extraction. First, the automatic detection of straight-line patterns is achieved by applying the Hough transform. Then, the discrete wavelet transform is proposed to break down spectrograms into sub-images of different scales, removing broadband noise and pellet injection signatures. The multiscale decomposition is subsequently extended to multiple directions using either 2D Fourier transforms or curvelets, achieving a high signal-to-noise ratio in spectrograms and eliminating undesired frequency sweeps of toroidal Alfvén eigenmodes antenna. Once MHD activity is successfully enhanced, a pipeline of algorithms for ridge detection, thresholding and labeling perform a segmentation of the image, automatically labeling individual modes. This study demonstrates the effectiveness of CV algorithms for the identification of MHD modes. The use of such algorithms may potentially help in the analysis process and the creation of large databases of modes.

Improved CycleGAN for Mixed Noise Removal in Infrared Images

Infrared images are susceptible to interference from a variety of factors during acquisition and transmission, resulting in the inclusion of mixed noise, which seriously affects the accuracy of subsequent vision tasks. To solve this problem, we designed a mixed noise removal algorithm for infrared images based on improved CycleGAN. First, we proposed a ResNet-E Block that incorporates the EMA (Efficient Multi-Scale Attention Module) and build a generator based on it using the skip-connection structure to improve the network’s ability to remove mixed noise of different strengths. Second, we added the PSNR (Peak Signal-to-Noise Ratio) as an extra calculation item of cycle consistency loss, so that the network can effectively retain the detailed information of infrared images while denoising. Finally, we conducted experimental validation on both synthetic noisy images and real noisy images, which proved that our algorithm can effectively remove the mixed noise in infrared images and the denoising effect is better than other similar methods.

ChickenSense: A Low-Cost Deep Learning-Based Solution for Poultry Feed Consumption Monitoring Using Sound Technology

This research proposes a low-cost system consisting of a hardware setup and a deep learning-based model to estimate broiler chickens’ feed intake, utilizing audio signals captured by piezoelectric sensors. The signals were recorded 24/7 for 19 consecutive days. A subset of the raw data was chosen, and events were labeled in two classes, feed-pecking and non-pecking (including singing, anomaly, and silence samples). Next, the labeled data were preprocessed through a noise removal algorithm and a band-pass filter. Then, the spectrogram and the signal envelope were extracted from each signal and fed as inputs to a VGG-16-based convolutional neural network (CNN) with two branches for 1D and 2D feature extraction followed by a binary classification head to classify feed-pecking and non-pecking events. The model achieved 92% accuracy in feed-pecking vs. non-pecking events classification with an f1-score of 91%. Finally, the entire raw dataset was processed utilizing the developed model, and the resulting feed intake estimation was compared with the ground truth data from scale measures. The estimated feed consumption showed an 8 ± 7% mean percent error on daily feed intake estimation with a 71% R2 score and 85% Pearson product moment correlation coefficient (PPMCC) on hourly intake estimation. The results demonstrate that the proposed system estimates broiler feed intake at each feeder and has the potential to be implemented in commercial farms.

Underwater object detection in noisy imbalanced datasets

Class imbalance occurs in the datasets with a disproportionate ratio of observations. The class imbalance problem drives the detection and classification systems to be more biased towards the over-represented classes while the under-represented classes may not receive sufficient learning. Previous works often deploy distribution based re-balancing approaches to address this problem. However, these established techniques do not work properly for underwater object detection where label noise commonly exists. In our experiments, we observe that the imbalanced detection problem may be caused by imbalance data distributions or label noise. To deal with these challenges, we first propose a noise removal (NR) algorithm to remove label noise in the datasets, and then propose a factor-agnostic gradient re-weighting algorithm (FAGR) to address the imbalanced detection problem. FAGR provides a rebalanced gradient to each class, which encourages the detection network to treat all the classes equally whilst minimising the detection discrepancy. Our proposed NR+FAGR framework achieves state-of-the-art (SOAT) performance on three underwater object datasets due to its high capacity in handling the class imbalance and noise issues. The source code will be made available at: https://github.com/IanDragon.

Generating realistic data through modeling and parametric probability for the numerical evaluation of data processing algorithms in two-dimensional chromatography

BackgroundComprehensive two-dimensional chromatography generates complex data sets, and numerous baseline correction and noise removal algorithms have been proposed in the past decade to address this challenge. However, evaluating their performance objectively is currently not possible due to a lack of objective data. ResultTo tackle this issue, we introduce a versatile platform that models and reconstructs single-trace two-dimensional chromatography data, preserving peak parameters. This approach balances real experimental data with synthetic data for precise comparisons. We achieve this by employing a Skewed Lorentz-Normal model to represent each peak and creating probability distributions for relevant parameter sampling. The model's performance has been showcased through its application to two-dimensional gas chromatography data where it has created a data set with 458 peaks with an RMSE of 0.0048 or lower and minimal residuals compared to the original data. Additionally, the same process has been shown in liquid chromatography data. SignificanceData analysis is an integral component of any analytical method. The development of new data processing strategies is of paramount importance to tackle the complex signals generated by state-of-the-art separation technology. Through the use of probability distributions, quantitative assessment of algorithm performance of new algorithms is now possible. Therefore, creating new opportunities for faster, more accurate, and simpler data analysis development.

Split Bregman quantum noise removal algorithm for 3D reconstruction of neutron computed tomography image

The low intensity of the neutron source for neutron computed tomography (CT) results in a long acquisition time for a single projection, which causes the neutron projection data to contain a large amount of quantum noise. Quantum noise will degrade the quality of neutron CT reconstruction images. Therefore, an efficient quantum noise removal algorithm must be used in CT reconstruction. In this paper, an efficient quantum noise removal algorithm for neutron CT 3D image reconstruction is proposed by analysing classical image processing algorithms and quantum image processing algorithms, which employs the maximum likelihood expectation maximization to reconstruct the image and split Bregman algorithm to solve for the total variation (MLEM-SBTV). Experimental results show that MLEM-SBTV performs well in removing quantum noise and reconstructing the detailed structure of images.

Investigation on the elimination of striations in NDE of glass fiber composites

Glass fiber composites are widely used due to their unique performance advantages. However, defects in glass fiber composites can significantly affect their performance, making nondestructive evaluation necessary. During the X-ray spectral imaging of glass fiber composites, the 0/90° cross-stacking mode of glass fiber cloth results in irregular horizontal and vertical stripe noise, which seriously reduces the clarity of defect detection. Therefore, an appropriate algorithm is required to remove the noise. In this study, by analyzing the Fourier spectrum of X-ray spectral images of glass fiber composites, it was found that the stripe information is concealed in the vertical and horizontal bright lines in the middle of the Fourier spectrum image. A novel cross-sector filter was designed, and a stripe noise removal algorithm based on the cross-sector filter was proposed. The degree of the filter’s central angle can be adjusted according to the distribution of the stripe noise in the frequency domain, which removes the noise without losing much useful image information.

Digital image noise removal towards soybean and cotton plant disease using image processing filters

The processing of noisy images does not provide accurate outcomes. In most existing techniques, many filters have been employed to eradicate the noises and enhance the image quality. However, most of the works fail to generate enhanced outcomes because of degraded capability and increased time consumption. Hence, in the proposed research work, a novel pre-processing method called the optimization assisted cascaded filtering approach (OptCFA) is introduced. In the proposed Opt_CFA model, the Gaussian Amended Bilateral filter (GABF) is designed where the multilevel thresholds are calculated for the GABF outcomes. The filter parameters are optimized using the Amended Pelican optimization algorithm (AmPel) for first stage noise removal. The outcomes are then cascaded with an Extended Savitzky-Golay filter (E_SGF) for second stage noise removal. The proposed Opt_CFA model is a single approach with the integration of GABF, AmPel and E_SGF methods. In the results section, some existing pre-processing filters, like the median, bilateral, and Gaussian filters, are analyzed for comparison. The individual performance of GABF and E_SGF methods used in the proposed model are also analyzed. Through the proposed technique, the noises can be extensively removed, and the image quality can be greatly enhanced. Several existing filters are compared with the proposed model, whereas the results are analyzed using PYTHON.

An improved prediction model for COD measurements using UV-Vis spectroscopy.

In the 21st century, although water quality has been improved in the last two decades, water pollution by organic contaminants has remained a non-negligible issue in China, so Chemical-Oxygen Demand (abbreviated as COD, unit: mg L-1) is often used as the main index to measure the degree of surface water pollution. UV-Vis spectroscopy, as a sensitive and rapid analytical technique, is a green detection technology suitable for automatic online COD detection equipment. However, due to the complex composition of surface water, the interference degree of the UV-Vis spectrum caused by turbidity is strongly correlated with the size, type and color of particulate matter in the solution, which results in noise sensitivity and poor generalization of the current detection model. Therefore, the main purpose of this research is to improve the traditional detection model performance by using deep learning and a spectrum preprocessing algorithm. Firstly, we used an improved noise filter based on discrete wavelet transforms to solve the noise sensitivity. Secondly, we proposed a novel COD detection network to address poor generalization. Thirdly, we collected a total of 2259 water samples' UV-Vis absorption spectra and corresponding COD as a dataset. Then, we pipelined the improved noise removal algorithm and proposed COD detection network, as a complete COD prediction model. Finally, the experiment on the dataset shows that the COD prediction model has a good performance in terms of both noise tolerance and accuracy.

Raspberry Pi-based design of intelligent household classified garbage bin

Until now, waste pollution is a global problem. The first and foremost part of the solution is waste classification, as it can effectively reduce the output of waste, so it is particularly important to classify household garbage in the household. However, classification of household garbage is a difficult task for people, and there are not enough designs and researches on automatic garbage classification bins at home. In this study, we designed and built a light and convenient intelligent garbage bin using devices such as motors, turn plates and a camera for classifying household garbage. Compared with the traditional garbage classification methods, two improvements in the algorithm are made, including the combination of the EfficientNetB2 model with the parallel mixed-attention mechanism and the design of the background noise removal algorithm. Finally, the algorithm is disposed to the Raspberry Pi to compose the complete classification system. The accuracy of this classification system in the test set was 93.38% and the results show that this bin can effectively distinguish recyclable waste, kitchen waste, hazardous waste and other waste, and is suitable for daily life garbage identification at home.

Three-Dimensional Reconstruction Based on Multiple Views of Structured Light Projectors and Point Cloud Registration Noise Removal for Fusion.

Structured light technology is typical for capturing 3D point cloud data. This paper proposes a 3D reconstruction system to obtain point cloud data of complex objects based on nine-order Gray code and an eight-step structured light projection combined with a phase shift and phase unwrapping method. In this system, two projectors serve as bilateral projectors for structured light, along with a camera and rotating platforms. These components were used to obtain point cloud data from multiple perspectives, which helps avoid the shadow areas caused by a single projection angle and provides complementary point cloud data. The point clusters scanned under each perspective were transformed into the same coordinate system. Furthermore, a distance-based point cloud noise removal algorithm was proposed to optimize platform noise and facilitate point cloud data fusion. The experimental results proved that the system effectively captures 3D point cloud data for complex objects. The dimensional quantitative analysis of an aero engine blade was also performed.

A Novel Adaptive Swallow Swarm Optimisation Method for Medical Image Segmentation in Brain MRI Using Machine Learning

Automatic segmentation has to be effective to provide sufficient results for clinical use due to inherent challenges involved in medical images, including poor image quality, different imaging, segmentation protocols or variations in patients. Further, they are reduced to previously invisible classes by a lack of image-specific adaptation and the absence of generality. When handling some complex cases, it may lead to ineffectiveness, taking into account the differences in distribution between training and test data. To solve these issues in the proposed work, noise removal and segmentation algorithm is introduced by using Adaptive Swallow Swarm Optimization (ASSO) with Convolutional Neural Network (CNN) and is called as ASSO-CNNseg to find traces of cancer in Brain Tumour images. In the noise removal step nonlinear filtering algorithm is introduced to remove noises from the image samples efficiently. After the removal of noises from the image samples, then segmentation is performed by using the deep learning algorithm consists of some steps: image-based fine tuning, interaction-oriented uncertainty, and segmentation process. In the fine-tuning algorithm, ASSO algorithm is introduced for weighted loss feature taking the network and interaction-based uncertainty taken into account during image-dependent fine-tuning. Next, here the initial effort make use of CNNseg to tackle their earlier unknown objects are the image segmentation context is presented. The experimental outcomes demonstrate the highly accurate results of the proposed ASSO-CNNseg method with less number interactions between users and less consumption of time for user compared to classical interactive segmentation techniques such as Slic-seg and BIFSeg.

A Tremor Suppression and Noise Removal Algorithm for Microscopic Robot-Assisted Cataract Surgery

Ophthalmic surgery consists of a series of precise manipulations performed using a surgical microscope. A surgical robot with a master–slave paradigm is commonly adopted to improve the precision and guidance intelligence for ophthalmic surgery; however, the surgeon's hand tremor, misoperation, inherent characteristics of the robot, and environmental interference may cause tremors of the end-effector of the surgical manipulator, thus increasing the risk of soft tissue damage and poor prognosis for the patient. A symmetrical threshold noise reduction combiner (STIC) is proposed to suppress hand tremors and reduce environmental noise interference. Additionally, the inverse kinematic model of the eight-degrees-of-freedom surgical robot for cataract surgery is developed, with the constraints of continuous circular capsulorhexis manipulation. Simulation results show that the signal-to-noise ratio of the STIC is increased from 35.3907 to 57.0158 dB, whereas the maximum, average, standard deviation, and root mean square of the output of the STIC are decreased by 64.79%, 77.24%, 74.50%, and 75.86%, respectively. Three sets of in vitro experimental results show that these four indicators of the STIC performance are reduced by 59.73%, 76.57%, 57.23%, and 65.02%, respectively. These results validate the feasibility and effectiveness of the proposed method on hand tremor suppression and inherent high-frequency disturbance elimination, thereby enhancing the accuracy of the microsurgical operation.

Quarter match non-local means algorithm for noise removal

The notion of improving plays out in many forms in our lives. We look for better quality, faster speed, and leisurelier connections. To achieve our desired goals, we must ask questions. How to make a process stronger? How to make a process more efficient? How to make a process more effective? Image denoising plays a vital role in many professions and understanding how noise can be present in images has led to multiple denoising techniques. These techniques include total variation regularization, non-local regularization, sparse representation, and low-rank minimization just to name a few. Many of these techniques exist because of the concept of improvement. First, we have a change (problem). This change invokes thoughts and questions. How these changes occur and how they are handled play an essential role in the realization or malfunction of that process. With this understanding, first, we look to fully understand the process to achieve success. As it relates to image denoising, the non-local means is incredibly effective in image reconstruction. In particular, the non-local means filter removes noise and sharpens edges without losing too many fine structures and details. Also, the non-local means algorithm is amazingly accurate. Consequently, the disadvantage that plagues the non-local means filtering algorithm is the computational burden and it is due to the non-local averaging. In this paper, we investigate innovative ways to reduce the computational burden and enhance the effectiveness of this filtering process. Research examining image analysis shows there is a battle between noise reduction and the preservation of actual features, which makes the reduction of noise a difficult task. For exploration, we propose a quarter-match non-local means denoising filtering algorithm. The filters help to classify a more concentrated region in the image and thereby enhance the computational efficiency of the existing non-local means denoising methods and produce an enriched comparison for overlying in the restoration process. To survey the constructs of this new algorithm, the authors use the original non-local means filtering algorithm, which is coined, “State of the Art” and other selective processes to test the effectiveness and efficiency of the new model. When comparing the original non-local means with the new quarter match filtering algorithm, on average, we can reduce the computational cost by half, while improving the quality of the image. To further test our new algorithm, medical resonance (MR) and synthetic aperture radar (SAR) images are used as specimens for real-world applications.

Automated tuning of denoising algorithms for noise removal in chromatograms

Different algorithms, such as the Savitzky-Golay filter and Whittaker smoother, have been proposed to improve the quality of experimental chromatograms. These approaches avoid excessive noise from hampering data analysis and as such allow an accurate detection and quantification of analytes. These algorithms require fine-tuning of their hyperparameters to regulate their roughness and flexibility. Traditionally, this fine-tuning is done manually until a signal is obtained that removes the noise while conserving valuable peak information. More objective and automated approaches are available, but these are usually method specific and/or require previous knowledge.In this work, the L-and V-curve, k-fold cross-validation, autocorrelation function and residual variance estimation approach are introduced as alternative automated and generally applicable parameter tuning methods. These methods do not require any previous information and are compatible with a multitude of denoising methods. Additionally, for k-fold cross-validation, autocorrelation function and residual variance estimation, a novel implementation based on median estimators is proposed to handle the specific shape of chromatograms, typically composed of alternating flat baselines and sharp peaks. These tuning methods are investigated in combination with four denoising methods; the Savitsky-Golay filter, Whittaker smoother, sparsity assisted signal smoother and baseline estimation and denoising using sparsity approach.It is demonstrated that the median estimators approach significantly improves the denoising and information conservation performance of relevant smoother-tuner combinations up to a factor 4 for simulated datasets and even up to a factor 10 for an experimental chromatogram. Moreover, the parameter tuning methods relying on residual variance estimation, k-fold cross-validation and autocorrelation function lead to similar small root-mean squared errors on the different simulated datasets and experimental chromatograms. The sparsity assisted signal smoother and baseline estimation and denoising using sparsity approach, which both rely on the use of sparsity, systematically outperform the two other methods and are hence most appropriate for chromatograms.

Enabling Boomless CubeSat Magnetic Field Measurements With the Quad‐Mag Magnetometer and an Improved Underdetermined Blind Source Separation Algorithm

AbstractIn situ magnetic field measurements are often difficult to obtain due to the presence of stray magnetic fields generated by spacecraft electrical subsystems. The conventional solution is to implement strict magnetic cleanliness requirements and place magnetometers on a deployable boom. However, this method is not always feasible on low‐cost platforms due to factors such as increased design complexity, increased cost, and volume limitations. To overcome these problems, we propose using the Quad‐Mag CubeSat magnetometer with an improved Underdetermined Blind Source Separation (UBSS) noise removal algorithm. The Quad‐Mag consists of four magnetometer sensors in a single CubeSat form‐factor card that allows distributed measurements of stray magnetic fields. The UBSS algorithm can remove stray magnetic fields without prior knowledge of the magnitude, orientation, or number of noise sources. UBSS is a two‐stage algorithm that identifies signals through cluster analysis and separates them through compressive sensing. We use UBSS with single‐source point detection to improve the identification of noise signals and iteratively‐weighted compressed sensing to separate noise signals from the ambient magnetic field. Using a mock CubeSat, we demonstrate in the lab that UBSS reduces four noise signals producing more than 100 nT of noise at each magnetometer to below the expected instrument resolution (5 nT at 65 Hz). Additionally, we show that the integrated Quad‐Mag and improved UBSS system works well for 1U, 2U, 3U, and 6U CubeSats in simulation. Our results show that the Quad‐Mag and UBSS noise cancellation package enables high‐fidelity magnetic field measurements from a CubeSat without a boom.

Research on Multilevel Filtering Algorithm Used for Denoising Strong and Weak Beams of Daytime Photon Cloud Data with High Background Noise

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) is equipped with the Advanced Topographic Laser Altimeter System (ATLAS), which can achieve high-precision ground detection. However, due to its low pulse energy and high sensitivity, it is also affected by noise when obtaining data. Therefore, data denoising is critical to the subsequent processing and application. In this study, a multilevel filtering algorithm is proposed to denoise the daytime photon cloud data with high background noise. Firstly, the Random Sample Consensus (RANSAC) algorithm is used to roughly denoise the daytime photon cloud data with high background noise, and a signal point cloud buffer is established to remove most of the noise points. Subsequently, the horizontal continuity parameter is calculated based on the photon cloud data following the rough denoising process. Based on this parameter, the shape and size of the search domain of the results of the subsequent fine denoising algorithm are adaptively improved. Finally, three filtering directions (a horizontal direction, an intra-group unified direction, and an adaptive direction for each photon) are proposed, and a hybrid algorithm combining the Ordering Points to Identify the Clustering Structure (OPTICS) density clustering algorithm and the Relative Neighboring Relationship K-nearest neighbors-based noise removal (RNR−KNNB) algorithm is employed to accurately denoise the photon cloud data in the three filtering directions. Furthermore, the RANSAC algorithm based on a sliding overlap window is used to remove outliers for the weak beam fine denoising photon cloud data. The results indicate that, for the strong beams, the denoising accuracy of the multilevel filtering algorithm in the three filtering directions is comparable (Rs ≥ 0.96, F ≥ 0.67), and they are all better than that of the ATL08 algorithm (Rs/Rn/p/F = 0.85/0.67/0.52/0.65). For weak beams, the denoising accuracy of the multilevel filtering algorithm in the horizontal direction and the intra-group unified direction is similar (Rs = 0.92, F = 0.69), and it is superior to the denoising results in the adaptive direction of each photon and the ATL08 algorithm (Rs/Rn/p/F = 0.94/0.84/0.51/0.65, 0.88/0.87/0.55/0.67, respectively). For strong and weak beams, the p-value and F-value of the denoising results of multilevel filtering algorithms in three different filtering directions increase with the increase of SNR value. It is demonstrated that SNR is an important factor affecting the denoising results of algorithms. The multilevel filtering algorithm proposed in the study can effectively achieve precise denoising of daytime photon cloud data with high background noise, and the three different filtering directions have different effects on the denoising results of strong and weak beam photon cloud data. This can provide technical and methodological guidance for subsequent photon cloud data filtering processing.