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Due to the influence of illumination, noise, distortion and other factors on monocular vision images, the image quality is reduced, the difficulty of image information extraction is high, and there are often errors and uncertainties in background segmentation, which affect the effect of monocular vision image background virtualization. Therefore, a new depth information extraction monocular vision image automatic hierarchical background virtualization method is studied to improve the effect of background virtualization. The depth information map is extracted by anisotropic thermal diffusion equation. The morphology is used to fill the tiny holes in the depth information map, and its smoothing process is used to determine the image depth range, automatically layer the depth information map, and obtain the foreground layer and background layer. The background layer is virtualized by Gaussian blur operation. Pyramid image fusion method is used to fuse the foreground layer and the blurred background layer to complete the background virtualization of monocular vision image. Experimental results have shown that this method can effectively improve the clarity of depth information map edges, preserve a large amount of image edge information, and have high structural similarity, with an average value of 0.96. The efficiency is high, and the background virtualization time is only 15 ms. [ABSTRACT FROM AUTHOR]

Concealing sensitive information inside multimedia is very interesting in secure communication due to its wide application. This study discusses the different difficulties involved in embedding the ciphertext inside video frame without affecting the imperceptibility and the strength resistance against different cyberattacks. The main goal of this study is to suggest a novel technique to conceal encrypted data inside video frames securely. This is done by combining the Laplacian of Gaussian (LoG) edge detection algorithm and Chacha20 encryption algorithm. LoG facilitates to detect the suitable location inside video frame for concealing data. While ChaCha20 is used to encrypt data before embedding them inside these locations, which augmenting data security. The proposed method involves sequence of steps involving detecting edges inside video frames, determining the suitable edges for concealing data, encrypting data by XORing it with the encryption key which is generated using ChaCha20 algorithm, embedding the encrypted data inside the determined edges, and then reconstruct the video frame to rebuild the video that involves the concealed encrypted data and keeping the lowest level of visual distortion. The experimental results showed that combining these two approaches provide fast, robust, and secure method, which can be seen by evaluating the system using mean square error, peak signal-to-noise ratio, correlation, number of pixels change rate, unified average changing intensity, and entropy, these evaluation metrics provide excellent results. This study suggests a strong and novel method to embed the encrypted data inside video frames which can be employed in secure communication, copyright protection, and data authentication. Merging LoG and ChaCha20 algorithms produce perfect results in both security and visual perception quality, that provides a means for farther achievement in secure data embedding techniques.

Blurring is a significant concern in electro-optical satellite imagery due to its negative effect on image quality, which is caused by an undesirable loss of bandwidth.Blurred images and compromised image quality may result from atmospheric distortions, camera aberrations, and relative motion during the imaging process. Deblurring is thus a process used to restore deteriorated images, reduce blur, and recover the original image. The major type of blur explored in this study is Gaussian blur, and its effects on the image are investigated by applying the blur in equal proportions. Furthermore, influence of blur on the Modulation Transfer Function (MTF) of an electro-optic satellite image as well as the impacts of deblurring techniques, namely the Richardson-Lucy Deconvolution Algorithm, the Regularization Filter, and the Blind Deconvolution Algorithm on MTF values, wereexplored.

Structural health monitoring is an effective method to evaluate the safety status of dams. Measurement error is an important factor which affects the accuracy of monitoring data modeling. Processing the abnormal monitoring data before data analysis is a necessary step to ensure the reliability of the analysis. In this paper, we proposed a method to process the abnormal dam displacement monitoring data on the basis of matrix manipulation and Cuckoo Search algorithm. We first generate a scatter plot of the monitoring data and exported the matrix of the image. The scatter plot of monitoring data includes isolate outliers, clusters of outliers, and clusters of normal points. The gray scales of isolated outliers are reduced using Gaussian blur. Then, the isolated outliers are eliminated using Ostu binarization. We then use the Cuckoo Search algorithm to distinguish the clusters of outliers and clusters of normal points to identify the process line. To evaluate the performance of the proposed data processing method, we also fitted the data processed by the proposed method and by the commonly used 3- σ method using a regression model, respectively. Results indicate that the proposed method has a better performance in abnormal detection compared with the 3- σ method. [ABSTRACT FROM AUTHOR]

Biometric-based identity verification systems have gained substantial attention due to their ability to provide high-level security. Among these systems, iris recognition systems have emerged as one of the most accurate and complex verification approaches. However, an ideal recognition system with a short processing time has not yet been reported in the literature because of the trade-offs involved. In this article, a novel framework for an iris recognition system is proposed based on hybrid deep neural network (DNN) classification-based Linear Discriminant Analysis (LDA) for feature extraction. The developed system includes unique pre-processing steps for both training and testing datasets, which are modulated by greyscale conversation, Gaussian blurring, binary imaging, contour segmentation and resizing. The proposed LDA-DNN provides high accuracy and stability for human identity verification with a short processing time. The proposed model accomplishes this task perfectly without any loss, which is unique among this type of approach. The results are validated via five computed measurement parameters. Experimental results are obtained by applying the model to four typical existing databases for powerful validation. Moreover, the proposed LDA-DNN framework results are compared with outcome measures obtained for state-of-the-art iris recognition approaches. The experimental results illustrate the success and power of the proposed LDA-DNN model, which attains an accuracy of 100% within a time of 70 ms, corresponding to an ideal recognition result that validated using several databases. Furthermore, this work provides a model within a unique property, in which it does not require a specific database or measurement parameters for evaluation. [ABSTRACT FROM AUTHOR]

Blurring is a significant concern in electro-optical satellite imagery due to its negative effect on image quality, which is caused by an undesirable loss of bandwidth. Blurred images and compromised image quality may result from atmospheric distortions, camera aberrations, and relative motion during the imaging process. Deblurring is thus a process used to restore deteriorated images, reduce blur, and recover the original image. The major type of blur explored in this study is Gaussian blur, and its effects on the image are investigated by applying the blur in equal proportions. Furthermore, influence of blur on the Modulation Transfer Function (MTF) of an electrooptic satellite image as well as the impacts of deblurring techniques, namely the Richardson-Lucy Deconvolution Algorithm, the Regularization Filter, and the Blind Deconvolution Algorithm on MTF values, were explored. [ABSTRACT FROM AUTHOR]

This paper presents a method for evaluating the quality of images altered by Gaussian blur. The method is based on the observation of bokeh mode images where the region of interest (foreground) is sharp, while the remaining parts (background) are intentionally blurred to enhance the perceptual quality of the image. The blurriness of the background increases attention towards the foreground part of the image. The proposed quality metric is obtained by combining the attention factor and the sharpness of the region of interest. The accuracy, in terms of Spearman's-rank-order correlation-coefficient (SROCC), for popular and publicly available databases such as LIVE, VCL, TID2008, CSIQ, and TID2013, is 0.963, 0.925, 0.900, 0.930, and 0.930, respectively. The proposed method achieves high and consistent Spearman's rank-order correlation coefficient (SROCC) values compared to the majority of state-of-the-art algorithms. Furthermore, in terms of speed, the proposed method surpasses other state-of-the-art methods. The MATLAB code of the proposed metric is publicly available at https://drive.google.com/drive/folders/1SRmUp0N157Ati9l3kV13uoCxw5PhMgQn?usp=sharing. [ABSTRACT FROM AUTHOR]

Detection of melanoma in the early stage can provide the patients with more promising treatments. Numerous methods have been proposed for detecting melanoma using computational approaches; however, the application of deep learning in this field is incipient and needs to be assessed further. In this paper, we introduce MDDC, a four-step deep learning-based pipeline for melanoma detection using digital images. MDDC removes image noise using discrete wavelet transform, pre-processes and extends the generalizability using a Gaussian blur filter and image transformation techniques, and finally computes the probability of melanoma in digital images using a convolutional neural network. The model performance is evaluated by considering various CNN architectures and three validation scenarios to find the best CNN architecture. Moreover, we used the transfer learning technique to improve the model performance and generalize it for working on another dataset. MDDC can handle noisy images and is insensitive to image transformations. The validation of MDDC led to 0.96 Accuracy, 0.96 Recall, 0.954 Specificity, 0.99 AUC, and 0.99 AUPR. In addition, further evaluation of the proposed method with state-of-the-art methods verifies that MDDC outperforms other methods. Consequently, MDDC can efficiently and quickly detect melanoma in digital images. [ABSTRACT FROM AUTHOR]

Structural health monitoring is an effective method to evaluate the safety status of dams. Measurement error is an important factor which affects the accuracy of monitoring data modeling. Processing the abnormal monitoring data before data analysis is a necessary step to ensure the reliability of the analysis. In this paper, we proposed a method to process the abnormal dam displacement monitoring data on the basis of matrix manipulation and Cuckoo Search algorithm. We first generate a scatter plot of the monitoring data and exported the matrix of the image. The scatter plot of monitoring data includes isolate outliers, clusters of outliers, and clusters of normal points. The gray scales of isolated outliers are reduced using Gaussian blur. Then, the isolated outliers are eliminated using Ostu binarization. We then use the Cuckoo Search algorithm to distinguish the clusters of outliers and clusters of normal points to identify the process line. To evaluate the performance of the proposed data processing method, we also fitted the data processed by the proposed method and by the commonly used 3-σ method using a regression model, respectively. Results indicate that the proposed method has a better performance in abnormal detection compared with the 3-σ method.

Cloud detection is an important part of remote sensing image processing. Excellent cloud detection methods can accurately extract clouds from remote sensing images, providing convenience for subsequent processing. Most of the traditional cloud detection methods have problems such as poor generalization ability and insufficient segmentation accuracy, and the detection results cannot well meet the requirements of follow-up work. In this paper, the cloud detection dataset is constructed by data enhancement method, and the cloud in remote sensing image is accurately detected by improving U-Net network model. Firstly, the sub-pixel image segmentation is achieved by using the superpixels segmentation method to further improve the accuracy of similar target aggregation and reduce cloud labeling errors. Then, the Gaussian blur method is improved to blur the remote sensing image around the cloud label adaptively, which weakens the influence of background on cloud detection and reduces the computing cost. Finally, VGG16 network is used to deepen the feature extraction part of U-Net network to extract multi-scale cloud feature information from remote sensing images and improve the cloud detection accuracy. A large number of experiments are carried out on GF-2 and MODIS remote sensing images and compared with other cloud detection methods. Experimental results show that the proposed method can accurately detect large area clouds and broken clouds in remote sensing images, with Dice value up to 94.6%, IoU value up to 89.8% and CPA value up to 94.2%. [ABSTRACT FROM AUTHOR]

The integration of deep learning methods, especially Convolutional Neural Networks (CNN), and Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) has been widely deployed in the field of radar signal processing. Nevertheless, these methods are frequently regarded as black-box models due to the limited visual interpretation of their internal feature representation and parameter organization. In this paper, we propose an innovative approach named C-RISE, which builds upon the RISE algorithm to provide a post-hoc interpretation technique for black-box models used in SAR Images Target Recognition. C-RISE generates saliency maps that effectively visualize the significance of each pixel. Our algorithm outperforms RISE by clustering masks that capture similar fusion features into distinct groups, enabling more appropriate weight distribution and increased focus on the target area. Furthermore, we employ Gaussian blur to process the masked area, preserving the original image structure with optimal consistency and integrity. C-RISE has been extensively evaluated through experiments, and the results demonstrate superior performance over other interpretation methods based on perturbation when applied to neural networks for SAR image target recognition. Furthermore, our approach is highly robust and transferable compared to other interpretable algorithms, including white-box methods. [ABSTRACT FROM AUTHOR]

Visual inspection of peripheral blood samples is a critical step in the leukemia diagnostic process. Automated solutions based on artificial vision approaches can accelerate this procedure, while also improving accuracy and uniformity of response in telemedicine applications. In this study, we propose a novel GBHSV-Leuk method to segment and classify Acute Lymphoblastic Leukemia (ALL) cancer cells. GBHSV-Leuk is a two staged process. The first stage involves pre-processing, which uses the Gaussian Blurring (GB) technique to blur the noise and reflections in the image. The second stage involves segmentation using the Hue Saturation Value (HSV) technique and morphological operations to differentiate between the foreground and background colors, which improve the accuracy of prediction. The proposed method attains 96.30% accuracy when applied on the private dataset, and 95.41% accuracy when applied on the ALL-IDB1 public dataset. This work would facilitate early detection of ALL cancer. [ABSTRACT FROM AUTHOR]

Diabetic Retinopathy is a serious complication arising in diabetes afflicted patients. Its effective treatment depends on early detection, and the course of action varies decisively with the intensity of the affliction. Computer-aided diagnosis helps to detect not only the presence or absence of the disease, but also the severity, making it easier for ophthalmologists to construct a treatment plan. Diabetic retinopathy grading is the task of classifying images of the eye's fundus of diabetic patients into 5 different grades ranging from 0-4 based on the severity of the disease. In this work, we propose a deep neural network architecture to address the grading problem. The method utilizes additional attention layer in the neural network model to capture the spatial relationship between the region of interests in the images during the training process to better discriminate between the different severity stage of the disease. Also, we analyze the impact of different image processing techniques on the classification results. We assessed the performance of our proposed method using a dataset of eye fundus images and obtained classification accuracy of 89.20% on average. This performance surpass that reported for other state-of-the-art methods on the same dataset. The effectiveness of the proposed method will facilitate the procedural workflow of identifying severe cases of diabetic retinopathy. [ABSTRACT FROM AUTHOR]

The integration of deep learning methods, especially Convolutional Neural Networks (CNN), and Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) has been widely deployed in the field of radar signal processing. Nevertheless, these methods are frequently regarded as black-box models due to the limited visual interpretation of their internal feature representation and parameter organization. In this paper, we propose an innovative approach named C-RISE, which builds upon the RISE algorithm to provide a post-hoc interpretation technique for black-box models used in SAR Images Target Recognition. C-RISE generates saliency maps that effectively visualize the significance of each pixel. Our algorithm outperforms RISE by clustering masks that capture similar fusion features into distinct groups, enabling more appropriate weight distribution and increased focus on the target area. Furthermore, we employ Gaussian blur to process the masked area, preserving the original image structure with optimal consistency and integrity. C-RISE has been extensively evaluated through experiments, and the results demonstrate superior performance over other interpretation methods based on perturbation when applied to neural networks for SAR image target recognition. Furthermore, our approach is highly robust and transferable compared to other interpretable algorithms, including white-box methods.

The visualization of flood disasters in virtual reality (VR) scenes is useful for the representation and sharing of disaster knowledge and can effectively improve users’ cognitive efficiency in comprehending disaster information. However, the existing VR methods of visualizing flood disaster scenes have some shortcomings, such as low rendering efficiency and poor user experience. In this paper, a tunnel vision optimization method for VR flood scenes based on Gaussian blur is proposed. The key techniques are studied, such as region of interest (ROI) calculation and tunnel vision optimization considering the characteristics of the human visual system. A prototype system has been developed and used to carry out an experimental case analysis. The experimental results show that the number of triangles drawn in a flood VR scene is reduced by approximately 30%–40% using this method and that the average frame rate is stable at approximately 90 frames per second (fps), significantly improving the efficiency of scene rendering and reducing motion sickness.

Problem of traffic overcrowding needs to be conveyed in a systematic way, notably in the country like India where this is a very grave problem due to enormous population. The attending traffic control systems that are used are either Physical controlled or instinctive controlling system with static time allocation. One requires huge man potential to operate and is less efficient and Destruction of time. In this project, image processing technique is used to control traffic overcrowding by instinctive allocating time to let out vehicles depending upon on the density of vehicles on roads. The proposed work uses Canny Edge detection, Gaussian Blur, Density calculation, Arduino UNO. [ABSTRACT FROM AUTHOR]

The discriminative information of fruit fly images often exists in a very fine region, resulting in hard extracting the desired features from the entire image level. Hence, it is a tough task to obtain the discriminative features in the fine-grained image for the classification of fruit fly images. To address this, here proposed a convolutional neural network of bilinear pooling based on feature fusion for the classification of fruit fly images. Firstly, Gaussian blur is performed on fruit fly images to reduce the detailed level of fruit fly images, which conveniently extracts high-level features. In the fruit fly images processed by Gaussian blur, the convolutional layers in the proposed model (i.e., consisting of CNN A and CNN B) are used for the features extraction from the processed images. Then, these extracted features are fused by using the matrix dot multiplication in the Bilinear layers. According to these fused features, the softmax layer performs the classification of fruit fly images. Experimental results on the 4000 image set containing different fruit fly morphology show that the propose method not only outperforms the state-of-the-art methods in the classification accuracy of fruit fly images, but also the precision, recall and score are above 94.87% on testing set and validation set. We find that image feature fusion has positive effects on promoting the accuracy of image classification. We also demonstrate that using feature connection operation after performing the matrix point multiplication operation is beneficial to feature fusion, instead of using feature connection operation directly. In addition, our findings indicates that convolutional neural networks easily obtain the desired features from the images performed by Gaussian blur. [ABSTRACT FROM AUTHOR]

Thousands of airplanes traverse the national airspace every day. Each flight has a unique trajectory in space and time that is typically pre-determined by a flight plan and can be inflexible. With air traffic density expected to increase in the future, NASA has been conducting research on technology that allows flexibility for airplanes to fly more optimal trajectories that can save time and fuel. A recent computer simulation study was conducted to investigate differences in an airspace with and without the technology. A visual comparison of the trajectories in each airspace was done using heatmaps. Initial inspection showed the heatmaps were quite sensitive to small differences among trajectories. In this paper, we demonstrate the use and efficacy of kernel smoothing for improved visual comparisons of high density and overlapping aircraft trajectories via heatmaps. [ABSTRACT FROM AUTHOR]

Although SISR (Single Image Super Resolution) problem can be effectively solved by deep learning based methods, the training phase often considers single degradation type such as bicubic interpolation or Gaussian blur with fixed variance. These priori hypotheses often fail and lead to reconstruction error in real scenario. In this paper, we propose an end-to-end CNN model RPSRMD to handle SR problem in multiple Gaussian degradations by extracting and using as side information a shared image prior that is consistent in different Gaussian degradations. The shared image prior is generated by an AED network RPGen with a rationally designed loss function that contains two parts: consistency loss and validity loss. These losses supervise the training of AED to guarantee that the image priors of one image with different Gaussian blurs to be very similar. Afterwards we carefully designed a SR network, which is termed as PResNet (Prior based Residual Network) in this paper, to efficiently use the image priors and generate high quality and robust SR images when unknown Gaussian blur is presented. When we applied variant Gaussian blurs to the low resolution images, the experiments prove that our proposed RPSRMD, which includes RPGen and PResNet as two core components, is superior to many state-of-the-art SR methods that were designed and trained to handle multi-degradation.

Visual inspection of peripheral blood samples is a critical step in the leukemia diagnostic process. Automated solutions based on artificial vision approaches can accelerate this procedure, while also improving accuracy and uniformity of response in telemedicine applications. In this study, we propose a novel GBHSV-Leuk method to segment and classify Acute Lymphoblastic Leukemia (ALL) cancer cells. GBHSV-Leuk is a two staged process. The first stage involves pre-processing, which uses the Gaussian Blurring (GB) technique to blur the noise and reflections in the image. The second stage involves segmentation using the Hue Saturation Value (HSV) technique and morphological operations to differentiate between the foreground and background colors, which improve the accuracy of prediction. The proposed method attains 96.30% accuracy when applied on the private dataset, and 95.41% accuracy when applied on the ALL-IDB1 public dataset. This work would facilitate early detection of ALL cancer.

Satellite imagery acquired from optical remote sensing systems is often suffered due to several types of blur, such as atmospheric turbulence blur, motion blur, and defocus. Any kind of blur degrades the image quality, as it reduces the sharpness of edges and texture, and hence, spatial resolution is also reduced. This degradation poses a challenge for further automated analysis using such blurred images. Image deconvolution methods are conventionally applied to blurry images to estimate the blur and restore the original image. In this inverse and ill-posed problem, the restoration quality relies on the correct estimation of the point spread function that caused blur in the image. We aim to restore the blurred satellite imagery, corrupted by Gaussian blur, using the deep learning framework and hence prove the efficacy of the proposed method over the traditional methods. This paper presents an investigative analysis of the satellite image deblurring problem and simultaneously tackles the problem of low-resolution satellite imagery. A deep convolutional neural network (CNN) architecture is proposed to remove the Gaussian blur artifacts from images and increase their resolution. Experimental evaluation is performed on a hyperspectral image dataset consisting of 8 different terrain types. Results obtained after deblurring the images are compared with state-of-the-art methods based on both subjective and objective image quality measures: peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). The image quality of deblurred images obtained by the proposed deep CNN method demonstrates improved performance over some of the existing CNN methods. [ABSTRACT FROM AUTHOR]

The visualization of flood disasters in virtual reality (VR) scenes is useful for the representation and sharing of disaster knowledge and can effectively improve users' cognitive efficiency in comprehending disaster information. However, the existing VR methods of visualizing flood disaster scenes have some shortcomings, such as low rendering efficiency and poor user experience. In this paper, a tunnel vision optimization method for VR flood scenes based on Gaussian blur is proposed. The key techniques are studied, such as region of interest (ROI) calculation and tunnel vision optimization considering the characteristics of the human visual system. A prototype system has been developed and used to carry out an experimental case analysis. The experimental results show that the number of triangles drawn in a flood VR scene is reduced by approximately 30%–40% using this method and that the average frame rate is stable at approximately 90 frames per second (fps), significantly improving the efficiency of scene rendering and reducing motion sickness. [ABSTRACT FROM AUTHOR]

In this study, the authors propose a method to calculate the consistency of alpha masking to assess the robustness of the matting algorithm. This study evaluates consistent alpha masks based on the Gaussian–Hermite moment in combination with gradient amplitude and gradient direction. The gradient direction describes the appearance and shape of local objects in the image, and the gradient amplitude accurately reflects the contrast and texture changes of small details in the image. They selected Gaussian blur, pretzel noise, and combined noise to destroy the image, and then evaluated the consistency of the original alpha mask and noise alpha mask. To determine the robustness of the matting algorithm, they assessed the degree of consistency of the alpha mask using three different evaluation levels. The experimental results show that noise has a greater impact on the performance of the matting algorithm, which shows a decreasing trend as the noise level in the image deepens. In noisy images, the traditional matting algorithm exhibits better robustness compared to the recently proposed trap matting algorithm. Different matting algorithms present different adaptations to different noises. [ABSTRACT FROM AUTHOR]

In this study, the authors propose a method to calculate the consistency of alpha masking to assess the robustness of the matting algorithm. This study evaluates consistent alpha masks based on the Gaussian–Hermite moment in combination with gradient amplitude and gradient direction. The gradient direction describes the appearance and shape of local objects in the image, and the gradient amplitude accurately reflects the contrast and texture changes of small details in the image. They selected Gaussian blur, pretzel noise, and combined noise to destroy the image, and then evaluated the consistency of the original alpha mask and noise alpha mask. To determine the robustness of the matting algorithm, they assessed the degree of consistency of the alpha mask using three different evaluation levels. The experimental results show that noise has a greater impact on the performance of the matting algorithm, which shows a decreasing trend as the noise level in the image deepens. In noisy images, the traditional matting algorithm exhibits better robustness compared to the recently proposed trap matting algorithm. Different matting algorithms present different adaptations to different noises.

Nowadays user devices such as phones, tablets etc. allows processing the images with help of high-end applications and softwares developed. Most of the times, the images are downscaled to make them compatible with these end devices. This leads to the loss of image quality. This loss of information on downscaling an image results in distortion of edges and while zoomed in results into a blurred image. As the edge detection is a basic step for many image processing applications such as object detection, object segmentation, object recognition, etc. It is necessary to know the impact of edge detection on downscaled image. In this paper, we are using Canny Edge detection method to detect the edges. The original images are downscaled using different interpolation methods. Canny Edge detection is applied on original images and downscaled images to compare the distortion in the edges. We used Structural Similarity Index Method for comparison. We are also comparing execution time taken by Canny Edge Detection on different interpolation methods to check for optimal interpolation method. We observed that the distortion in edges and time efficiency differ for different interpolation methods which are detailed below in the result section. As blurring is also a disadvantage of downscaling, we are applying Gaussian Blur on the images to compare the blurring due to Gaussian blur technique and blurring due to downscaling. [ABSTRACT FROM AUTHOR]

Abstract—In the study, we examine the problem of diagnosing distortions in the resulting image, the so-called "blind estimation" task. The questions of identification of the type and parameters of linear smoothing homogeneous distorting operators of three types are investigated: circular form with a rectangular profile, circular form with a Gaussian profile, and linear form with a rectangular profile. The identification of distortions is carried out by means of the image spectrum density analysis using the average radial profile and average directional profile. The diagnostic algorithms for each of the considered distortion types are proposed and studied. The influence of noise and data accuracy on the result reliability and the capabilities of diagnostic algorithms in the case of simultaneous superposition of several distortions are demonstrated. [ABSTRACT FROM AUTHOR]