Showing papers on "Impulse noise published in 2018"

Hyperspectral Image Restoration Via Total Variation Regularized Low-Rank Tensor Decomposition

Abstract: Hyperspectral images (HSIs) are often corrupted by a mixture of several types of noise during the acquisition process, e.g., Gaussian noise, impulse noise, dead lines, stripes, etc. Such complex noise could degrade the quality of the acquired HSIs, limiting the precision of the subsequent processing. In this paper, we present a novel tensor-based HSI restoration approach by fully identifying the intrinsic structures of the clean HSI part and the mixed noise part. Specifically, for the clean HSI part, we use tensor Tucker decomposition to describe the global correlation among all bands, and an anisotropic spatial–spectral total variation regularization to characterize the piecewise smooth structure in both spatial and spectral domains. For the mixed noise part, we adopt the $\ell _1$ norm regularization to detect the sparse noise, including stripes, impulse noise, and dead pixels. Despite that TV regularization has the ability of removing Gaussian noise, the Frobenius norm term is further used to model heavy Gaussian noise for some real-world scenarios. Then, we develop an efficient algorithm for solving the resulting optimization problem by using the augmented Lagrange multiplier method. Finally, extensive experiments on simulated and real-world noisy HSIs are carried out to demonstrate the superiority of the proposed method over the existing state-of-the-art ones.

Hyperspectral Image Denoising Using Local Low-Rank Matrix Recovery and Global Spatial–Spectral Total Variation

Abstract: Hyperspectral images (HSIs) are usually contaminated by various kinds of noise, such as stripes, deadlines, impulse noise, Gaussian noise, and so on, which significantly limits their subsequent application. In this paper, we model the stripes, deadlines, and impulse noise as sparse noise, and propose a unified mixed Gaussian noise and sparse noise removal framework named spatial–spectral total variation regularized local low-rank matrix recovery (LLRSSTV). The HSI is first divided into local overlapping patches, and rank-constrained low-rank matrix recovery is adopted to effectively separate the low-rank clean HSI patches from the sparse noise. Differing from the previous low-rank-based HSI denoising approaches, which process all the patches individually, a global spatial–spectral total variation regularized image reconstruction strategy is utilized to ensure the global spatial–spectral smoothness of the reconstructed image from the low-rank patches. In return, the globally reconstructed HSI further promotes the separation of the local low-rank components from the sparse noise. An augmented Lagrange multiplier method is adopted to solve the proposed LLRSSTV model, which simultaneously explores both the local low-rank property and the global spatial–spectral smoothness of the HSI. Both simulated and real HSI experiments were conducted to illustrate the advantage of the proposed method in HSI denoising, from visual/quantitative evaluations and time cost.

Structure-Adaptive Fuzzy Estimation for Random-Valued Impulse Noise Suppression

Abstract: Noise detection accuracy is crucial in suppressing random-valued impulse noise. Both false and miss detections determine the final estimation performance. Deterministic detection methods, which distinctly classify pixels into noisy or uncorrupted pixels, tend to increase the estimation error because some uncorrupted edge points are hard to discriminate from the random-valued impulse noise points. This paper proposes an iterative structure-adaptive fuzzy estimation (SAFE) for random-valued impulse noise suppression. This SAFE method is developed in the framework of Gaussian maximum likelihood estimation. The structure-adaptive fuzziness is reflected by two structure-adaptive metrics based on pixel reliability and patch similarity, respectively. The reliability metric for each pixel (as noise free) is estimated via a novel-minimal-path-based structure propagation to give full consideration of the spatially varying image structures. A robust iteration stopping strategy is also proposed by evaluating the reestimation error of the uncorrupted intensity information. The comparative experimental results show that the proposed structure-adaptive fuzziness can lead to effective restoration. An efficient implementation of this SAFE method is also realized via graphics-processing-unit-based parallelization.

A Survey on Various Median Filtering Techniques For Removal of Impulse Noise From Digital Image

Abstract: The elimination of noise from images becomes a trending field in image processing. Imagesmay got corrupted by random change in pixel intensity, illumination, or due to poor contrast and can't be used directly. Therefore, it is necessary to get rid of impulse noise presented inan image. In order to remove such impulse noise, Median based filters are commonly used. However, we use various types of median filters such as recursive median filter, iterative median filter, directional median filter, weighted median filter, adaptive median filter progressive switching median filter and threshold median filter. This paper will survey various median filtering techniques for excluding noisy pixel from a digital image.

Sparse and Low-Rank Decomposition of a Hankel Structured Matrix for Impulse Noise Removal.

Abstract: Recently, the annihilating filter-based low-rank Hankel matrix (ALOHA) approach was proposed as a powerful image inpainting method. Based on the observation that smoothness or textures within an image patch correspond to sparse spectral components in the frequency domain, ALOHA exploits the existence of annihilating filters and the associated rank-deficient Hankel matrices in an image domain to estimate any missing pixels. By extending this idea, we propose a novel impulse-noise removal algorithm that uses the sparse and low-rank decomposition of a Hankel structured matrix. This method, referred to as the robust ALOHA, is based on the observation that an image corrupted with the impulse noise has intact pixels; consequently, the impulse noise can be modeled as sparse components, whereas the underlying image can still be modeled using a low-rank Hankel structured matrix. To solve the sparse and low-rank matrix decomposition problem, we propose an alternating direction method of multiplier approach, with initial factorized matrices coming from a low-rank matrix-fitting algorithm. To adapt local image statistics that have distinct spectral distributions, the robust ALOHA is applied in a patch-by-patch manner. Experimental results from impulse noise for both single-channel and multichannel color images demonstrate that the robust ALOHA is superior to existing approaches, especially during the reconstruction of complex texture patterns.

A Robust Edge Detection Approach in the Presence of High Impulse Noise Intensity Through Switching Adaptive Median and Fixed Weighted Mean Filtering

Abstract: This paper introduces a robust edge detection method that relies on an integrated process for denoising images in the presence of high impulse noise. This process is shown to be resilient to impulse (or salt and pepper) noise even under high intensity levels. The proposed switching adaptive median and fixed weighted mean filter (SAMFWMF) is shown to yield optimal edge detection and edge detail preservation, an outcome we validate through high correlation, structural similarity index, and peak signal-to-noise ratio measures. For comparative purposes, a comprehensive analysis of other denoising filters is provided based on these various validation metrics. The non-maximum suppression method and new edge following maximum-sequence are the two techniques used to track the edges and overcome edge discontinuities and noisy pixels, especially in the presence of high-intensity noise levels. After applying predefined thresholds to the grayscale image, and thus obtaining a binary image, several morphological operations are used to remove the unwanted edges and noisy pixels and perform edge thinning to ultimately provide the desired edge connectivity, which results in an optimal edge detection method. The obtained results are compared with other existing state-of-the-art denoising filters and other edge detection methods in support of our assertion that the proposed method is resilient to impulse noise even under high-intensity levels.

Mixed Gaussian-impulse noise reduction from images using convolutional neural network

Abstract: The removal of mixed-noise is an ill-posed problem due to high level of non-linearity in the distribution of noise. Most commonly encountered mixed-noise is the combination of additive white Gaussian noise (AWGN) and impulse noise (IN) that have contrasting characteristics. A number of methods from the cascade of IN and AWGN reduction to the state-of-the-art sparse representation have been reported to reduce this common form of mixed-noise. In this paper, a new learning-based algorithm using the convolutional neural network (CNN) model is proposed to reduce the mixed Gaussian-impulse noise from images. The proposed CNN model adopts computationally efficient transfer learning approach to obtain an end-to-end map from noisy image to noise-free image. The model has a small structure yet it is capable of providing performance superior to that of the well established methods. Experimental results on different settings of mixed-noise show that the proposed CNN-based denoising method performs significantly better than the sparse representation and patch-based methods do both in terms of accuracy and robustness. Moreover, due to the lightweight structure, the denoising operation of the proposed CNN-based method is computationally faster than that of the previously reported methods.

Highly Accurate Image Reconstruction for Multimodal Noise Suppression Using Semisupervised Learning on Big Data

Abstract: Impulse noise corruption in digital images frequently occurs because of errors generated by noisy sensors or communication channels, such as faulty memory locations in devices, malfunctioning pixels within a camera, or bit errors in transmission. Although recently developed big data streaming enhances the viability of video communication, visual distortions in images caused by impulse noise corruption can negatively affect video communication applications. In addition, ${{sparsity}}$ , ${{density}}$ , and ${{multimodality}}$ in large volumes of noisy images have often been ignored in recent studies, whereas these issues have become important because of the increasing viability of video communication services. To effectively eliminate the visual effects generated by the impulse noise from the corrupted images, this study proposes a novel model that uses a devised cost function involving semisupervised learning based on a large amount of corrupted image data with a few labeled training samples. The proposed model qualitatively and quantitatively outperforms the existing state-of-the-art image reconstruction models in terms of the denoising effect.

Region Adaptive Fuzzy Filter: An Approach for Removal of Random-Valued Impulse Noise

Abstract: This paper proposes region adaptive fuzzy filter for removal of random-valued impulse noise (RVIN) from color images. It is observed from existing literature that the filter performance increases with improved accuracy of noise detection and better adaption of the filter parameters. Improved minimum mean value detection mechanism is proposed for better classification of noisy and nonnoisy pixels in context to the removal of RVIN from color images. The modified fuzzy filter considers the correlation among the color channels and recursively adapts itself in accordance with the local noise densities. This filter incorporates an adaption technique to determine the maximum allowable window size used during fuzzification and filtering. Rather than one efficient filtering step that has a potential for losing information, region selective second iteration of the filter is applied on highly corrupted regions so as to preserve more image details. Experimental results suggest that the proposed filter provides improved performance in terms of peak signal to noise ratio, normalized color difference, structural similarity index, feature similarity index for color images, and perceptual similarity than most of the state-of-the-art filters.

Joint weighted nuclear norm and total variation regularization for hyperspectral image denoising

Abstract: A hyperspectral image is typically corrupted by multiple types of noise including Gaussian noise and impulse noise. On the other hand, a hyperspectral image possesses a high correlation in its spec...

Recurrent Mechanism and Impulse Noise Filter for Establishing ANFIS

Abstract: In many real applications, building and updating adaptive neuro-fuzzy inference system (ANFIS) based on noisy measuring data sources need to be performed such that the filtering impulse noise (IN) from the initial datasets (IDSs) and establishing the ANFIS via the filtered IDS are carried out simultaneously. Focused on this purpose, in this paper, a novel recurrent mechanism as well as a solution for filtering IN based on Lyapunov stability theory is proposed to establish an adaptive online IN filter (AOINF). Using the AOINF, kernel fuzzy-C-means clustering method, and the least mean squares method, a cluster data space deriving from the filtered IDS is created to which the ANFIS is then formed. The recurrent mechanism executes filtering IN to build ANFIS and using the ANFIS as an updated-filter to filter IN synchronously until either the ANFIS converges to the desired accuracy or a stop condition is satisfied. Surveys, including identifying dynamic response of a magnetorheological damper via measuring datasets, are performed to evaluate the proposed method.

Mixed Noise Removal via Robust Constrained Sparse Representation

Abstract: In recent years, the sparse coding-based techniques have been widely used for image denoising. However, most of the sparse coding-based mixed noise reduction methods fail to take full advantage of the geometric structure of data samples. In other words, they neglect the common information shared by the similar patches in sparse coding. To address this concern, in this paper, we propose a robust constrained sparse representation (RCSR) method to remove mixed noise. By using the center coefficient of similar patches as the guider which is approximated by the coefficient of query patch in sparse coding, the geometric structure of data can be well preserved. Moreover, different from most existing two-stage mixed noise reduction methods that use explicit detectors to restrain impulse noise, the proposed RCSR adaptively adjusts the contribution of each pixel in the loss function to eliminate the influences of outliers. Experiments on the reconstruction of synthetic data and the removal of mixed noise in real images demonstrate the effectiveness of our proposed method.

Set-membership normalised least M-estimate spline adaptive filtering algorithm in impulsive noise

Abstract: This letter proposes a set-membership normalised least M-estimate algorithm based on Wiener spline adaptive filter (SAF). The proposed algorithm combines the set-membership framework and least-M estimate scheme, thus achieving faster convergence rate and effective suppression of impulsive noise on the filter weight and control point adaptation. Simulation results demonstrate that the proposed one exhibits more robust performance compared to the conventional SAF algorithms in an impulsive noise environment.

A New Unified Impulse Noise Removal Algorithm Using a New Reference Sequence-to-Sequence Similarity Detector

Abstract: Existing detectors are based on using the similarity of pixels or blocks to locate noisy pixels. Alternatively, this paper presents an approach that investigates the similarity property among sequences of pixels to establish a new reference sequence-to-sequence similarity (RSSS) impulse noise detector. Then, to harness the advantages of this new RSSS detector in high detection accuracy, a new unified image denoising algorithm (RSSS-I) is introduced to remove different types of impulse noise. In this approach, the RSSS detector locates the impulse noise, and then, three different median filters remove the detected impulse noise in a cascade framework. In this framework, an existing weighted median filter is utilized as the domain filter, and two new directional mean and “extreme” median filters are applied as the post-filter. Experimental results show the benefits of this cascade framework in improving the performance. Comparison results demonstrate that the RSSS-I outperforms several existing methods for the ability to accurately locate the positions of noise, retain edge information, inhibit residual artifacts from occurring, and generating denoised images with better quality.

Impulse Noise Removal from Medical Images by Two Stage Quaternion Vector Median Filter

Abstract: This paper presents a two-stage impulse noise removal filter from medical images. Quaternion is used to represent differences of two pixels. The pixels are sorted and assigned a rank based on the aggregated sum of pixel differences with other pixels inside the filtering window. The central pixel is considered as corrupted by an impulse if its rank is bigger than a predefined rank and the minimum difference between it and other pixels in the four edge direction inside the window is larger than a predefined threshold. The noisy pixel is replaced by output of vector median filter implemented using quaternion. For color images, both intensity and chromaticity components are used. Quaternion processes color images as single unit rather than as separated color channels. This preserves the correlation and three dimensional vector natures of the color channels. For grayscale medical images, the same algorithm is implemented by using the intensity difference between two pixels. Experimental results show improved performance of the proposed filter in suppressing the impulse noise while retaining the original image details comparing against other well-known filters.

An Adaptive Median Filtering of Salt and Pepper Noise based on Local Pixel Distribution

Abstract: Abstract. A selective adaptive median filter is proposed for the restoration of gray scale images that are highly corrupted by salt and pepper noise. In this paper, impulse noise removal using the standard median filter and its variants are analyzed. After detecting the salt and pepper noise pixels, the self-adaptive median filter is use to find a suitable window containing more non-noise pixels. This proposed algorithm provides much better results than that of the standard median filter, weighed median filter and switching mean median filter. The proposed algorithm is tested against different gray scale images and it gives better Peak Signal Noise Ratio (PSNR), Mean Square Error (MSE) and Structural Similarity Index (SSIM).

Hyperspectral Unmixing with Bandwise Generalized Bilinear Model

Abstract: Generalized bilinear model (GBM) has received extensive attention in the field of hyperspectral nonlinear unmixing. Traditional GBM unmixing methods are usually assumed to be degraded only by additive white Gaussian noise (AWGN), and the intensity of AWGN in each band of hyperspectral image (HSI) is assumed to be the same. However, the real HSIs are usually degraded by mixture of various kinds of noise, which include Gaussian noise, impulse noise, dead pixels or lines, stripes, and so on. Besides, the intensity of AWGN is usually different for each band of HSI. To address the above mentioned issues, we propose a novel nonlinear unmixing method based on the bandwise generalized bilinear model (NU-BGBM), which can be adapted to the presence of complex mixed noise in real HSI. Besides, the alternative direction method of multipliers (ADMM) is adopted to solve the proposed NU-BGBM. Finally, extensive experiments are conducted to demonstrate the effectiveness of the proposed NU-BGBM compared with some other state-of-the-art unmixing methods.

Spatially adaptive total generalized variation-regularized image deblurring with impulse noise

Abstract: Image deblurring with impulse noise is a typical ill-conditioned problem that requires regularization techniques to guarantee stable and high-quality imaging According to the statistical properties of impulse noise, an L1-norm data fidelity term and a total variation (TV) regularizer have been combined to contribute a popular regularization model However, traditional TV-regularized variational models usually suffer from staircase-like artifacts in homogenous regions resulting in visual quality degradation To eliminate undesirable artifacts, we propose a high-order variational model by replacing the TV with a detail-preserving total generalized variation (TGV) regularizer To further enhance imaging performance, the spatially adaptive regularization parameters are automatically selected, based on local image features to promote the high-order TGV-regularized variational model The resulting nonsmooth optimization problem is effectively handled using the alternating direction method of multipliers-based numerical method The proposed variational model has the capacity to remove blurring and impulse noise effects while maintaining fine image details Comprehensive experiments were conducted on both gray and color images to compare our proposed method with several state-of-the-art image restoration methods Experimental results have demonstrated its superior performance in terms of quantitative and qualitative image quality evaluations

Restoring highly corrupted images by impulse noise using radial basis functions interpolation

Abstract: Preserving details while restoring images highly corrupted by impulsive salt and pepper noise remains a challenging problem. The authors proposed an algorithm based on radial basis functions (RBFs) interpolation which estimates the intensities of corrupted pixels by their neighbours. In this algorithm, intensity values of noisy pixels in the corrupted image are first estimated using RBFs. Next, the image is smoothed. The proposed algorithm can effectively remove the highly dense, impulsive salt and pepper noise. Experimental results show the superiority of the proposed algorithm both in noise suppression and details preservation in comparison to the recent similar methods. Extensive simulations show better results measured by peak signal-to-noise ratio and structural similarity index, especially when the image is corrupted by very highly dense impulse noise.

Fingerprint Image Enhancement Method based on Adaptive Median Filter

Abstract: The traditional median filtering method uses a fixed filter window size method to remove the impulse noise in a fingerprint image. If the filtering window size is small, the traditional median filtering method will not filter out the impulse noise completely. If the filtering window size is large, the fingerprint image may become blurred. To solve the problem, a method based on adaptive median filter is proposed for fingerprint image enhancement processing and impulse noise removal in the paper. The use of adaptive median filtering to remove the impulse noise of the fingerprint image mainly involves three steps. First, the size of the adaptive median filter window is initialized, and it is judged whether the center pixel of the filter window in the fingerprint image is impulse noise. Second, the size of the filter window is determined based on the median value, the maximum value, and the minimum value within the filter window. Finally, median filtering is performed on the fingerprint image under the filter window size obtained in the previous steps, and the filter output value is used instead of the window center pixel value. The method is tested on rolled fingerprint images contaminated by impulse noise and fingerprint images contaminated by impulse noise from a crime scene. Experimental results show that the method based on adaptive median filter for fingerprint image enhancement outperforms the traditional median filtering method in filtering impulse noise performance.

Design of acoustic transmission along drill strings for logging while drilling data based on adaptive NC-OFDM

Abstract: This paper represents a new scheme using the drill strings as the medium to deliver logging information via a compressional acoustic wave. Considering the comb-type frequency characteristics of an acoustic channel, non-contiguous orthogonal frequency division multiplexing (NC-OFDM) has been selected as a promising technique for achieving high-rate transmission with high spectral efficiency and immunity to multipath channels. However, the irregular pilot design and high peak-to-average power ratio (PAPR) halted the widespread application of NC-OFDM. To overcome these two disadvantages, we propose an adaptive pilot design scheme in which the dynamic range of available frequency is pre-simulated and an improved discrete Fourier transformation based least square (PDFT-LS) channel estimation scheme to obtain the channel characteristics and an improved selected mapping (PSLM) scheme with low-complexity to reduce the PAPR of the transmitter are applied. Computer simulation and circuit test results are presented to demonstrate that the data transmission rate can be in excess of 500 bps along a 53.76 m experimental channel under the conditions of the simulated impulse noise and surface noise with a low bit error rate (BER), and the PAPR reduction performance can reach almost the same as that of a conventional SLM (CSLM) scheme.

Adaptive Real-Time Removal of Impulse Noise in Medical Images

Abstract: Noise is an important factor that degrades the quality of medical images. Impulse noise is a common noise caused by malfunctioning of sensor elements or errors in the transmission of images. In medical images due to presence of white foreground and black background, many pixels have intensities similar to impulse noise and hence the distinction between noisy and regular pixels is difficult. Therefore, it is important to design a method to accurately remove this type of noise. In addition to the accuracy, the complexity of the method is very important in terms of hardware implementation. In this paper a low complexity de-noising method is proposed that distinguishes between noisy and non-noisy pixels and removes the noise by local analysis of the image blocks. All steps are designed to have low hardware complexity. Simulation results show that in the case of magnetic resonance images, the proposed method removes impulse noise with an acceptable accuracy.

An efficient Gaussian Noise Reduction Technique For Noisy Images using optimized filter approach

Abstract: In the Multimedia era, removal of the Noises from an image becomes a key challenge in the field of Digital Image Processing (DIP) and Computer Vision. Noise may be mixed with an image during capturing time, transmission time or due to dust particle on the screen of capturing device. Therefore, removal of these unwanted signals from the image is urgently required for the better analysis of the image and the de-noised image is more meaningful for Object detection, Edge detection and many more. There are various types of image noise, however, Gaussian Noise and Impulse Noise are commonly found in the image. This work focuses on the outliers and Mean Filter to improve the performance for Gaussian noise reduction from the image. In experimental assessments, artificial noise has been mixed using MATLAB to MSRA (10k images) dataset, this dataset is used to evaluate our proposed technique. The experiment results show that the proposed approach improves the performance in noise reduction over other filter approaches.

Robust Adaptive Median Binary Pattern for noisy texture classification and retrieval

Abstract: Texture is an important cue for different computer vision tasks and applications. Local Binary Pattern (LBP) is considered one of the best yet efficient texture descriptors. However, LBP has some notable limitations, mostly the sensitivity to noise. In this paper, we address these criteria by introducing a novel texture descriptor, Robust Adaptive Median Binary Pattern (RAMBP). RAMBP based on classification process of noisy pixels, adaptive analysis window, scale analysis and image regions median comparison. The proposed method handles images with high noisy textures, and increases the discriminative properties by capturing microstructure and macrostructure texture information. The proposed method has been evaluated on popular texture datasets for classification and retrieval tasks, and under different high noise conditions. Without any train or prior knowledge of noise type, RAMBP achieved the best classification compared to state-of-the-art techniques. It scored more than $90\%$ under $50\%$ impulse noise densities, more than $95\%$ under Gaussian noised textures with standard deviation $\sigma = 5$, and more than $99\%$ under Gaussian blurred textures with standard deviation $\sigma = 1.25$. The proposed method yielded competitive results and high performance as one of the best descriptors in noise-free texture classification. Furthermore, RAMBP showed also high performance for the problem of noisy texture retrieval providing high scores of recall and precision measures for textures with high levels of noise.

Mixed Noise Removal Algorithm Combining Adaptive Directional Weighted Mean Filter and Improved Adaptive Anisotropic Diffusion Model

Abstract: A mixed noise removal algorithm combining adaptive directional weighted mean filter and improved adaptive anisotropic diffusion model is proposed. Firstly, a noise classification method is introduced to divide all pixels into two types as the pixels corrupted by impulse noise and the pixels corrupted by Gaussian noise. Then an adaptive directional weighted mean filter is developed to remove impulse noise, which can adaptively select the optimal direction template from twelve direction templates and replace the gray level of each impulse noise corrupted pixel by the weighted mean gray level of pixels on the optimal direction template. Finally, an improved adaptive anisotropic diffusion model is developed to remove Gaussian noise in the initial denoised image, which can finely classify image features as smooth regions, edges, corners, and isolated noises by characteristic parameters and variance parameter and conduct adaptive diffusion for different image features by designing reasonable eigenvalues of diffusion tensor. A large number of experimental results show that the proposed algorithm outperforms many existing main mixed noise removal methods in terms of image denoising and detail preservation.

An efficient nonlinear approach for removing fixed-value impulse noise from grayscale images

Abstract: Removal of salt and pepper noise has been one of the most interesting researches in the field of image preprocessing tasks; it has two simultaneous stringent demands: the suppression of impulses and the preservation of fine details. To address this problem, a scheme based on nonlinear filters is proposed; it consists of the introduction of a redescending M-estimator within the modified nearest neighbor filter. In order to analyze all pixels in the neighborhood, as well as to reduce the magnitude of the existing impulses, a redescending M-estimator is used; the remaining pixels are processed by the modified nearest neighbor filter to obtain the best estimation of a noise-free pixel. The impulsive suppression is applied on the entire image by using a sliding window; the local information obtained by this one also allows to calculate the thresholds that characterize the influence function tested in the redescending M-estimator. To suppress high density fixed-value impulse noise in large-size grayscale images, the proposal is implemented on a heterogeneous CPU–GPU architecture. The noise reduction and the processing time of the proposed approach are evaluated by extensive simulations; its effectiveness is verified by quantitative and qualitative results.