Intelligent Fault Diagnosis System for Radiographic Images …...A variety of automated defect...

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Intelligent Fault Diagnosis System for Radiographic Images of Valve Casting using Adaptive Neuro Fuzzy Inference System S.S.Khedkar a , G.K.Awari b , S.P.Untawale c , S.P.Trikal d a Yeshwantrao Chavan College of Engineering, Nagpur, Maharashtra, India b Tulshiram Gaikwad Patil College of Engineering, Nagpur, Maharashtra, India c Datta Meghe Institute of Technology and Engineering Research, Wardha, Maharashtra, India d Shree Sant Gajanan Maharaj College of Enginering, Shegaon, Maharashtra, India a [email protected], b [email protected], c [email protected], d [email protected] Abstract This paper presents an automated defect detection and classification system for radiographic images of valve casting. The proposed system provides solution on complex problems of radiographic image interpretation. The multiple morphological image processing implemented in this work eliminates the problem of over-segmentation as well as gives good results to determine shape and size of the flaws. The proposed system is a set of techniques dedicated to implement a system of automatic inspection of radiographic images of valve casting.The steps involved in automated defect detection and classification are: digitization of the films, image pre-processing directed mainly at the reduction/elimination of noise, contrast improvement and discriminate feature enhancement facing the interpretation, multi-level morphological image processing & segmentation of defect regions. Features are extracted from the segmented region and classification technique such as adaptive neuro fuzzy inference system (ANFIS) is implemented. The proposed methodology was tested for various valve castings in a casting producing company. Keywords: Radiographic Testing, Film Digitization, Image processing, Segmentation, ANFIS. Introduction Radioscopy rapidly became the accepted way for controlling the quality of die cast pieces. In casting manufacturing, shrinkages occur during the cooling of the molten metal which can lead to inhomogeneous regions within the work piece. These are noticeable, for example, by bubble-shaped voids or fractures. Voids occur when the liquid metal fails to fill up the die completely or molten metal flows too slowly, whereas fractures are caused by mechanical stresses when neighboring regions develop different temperature gradients. In addition, other casting defects can occur, such as blow holes, inclusions etc. Ferrous alloy castings, such as valve body is considered to be part relevant to operational safety. In order to ensure the safety of valve construction, the quality of valve components is absolutely indispensable. Since last two decades, radiographic testing became the accepted method for the quality control of castings via visual or computer-aided analysis of X-ray images [1]. The purpose of this non-destructive testing method is to locate casting defects which may be located inside the piece and are thus not detectable to the naked eye [2] It is particularly important for critical applications where valve casting failure can be resulted in to sudden terrible disaster, such as in petrochemical refineries, oil and natural gas plants, and pneumatic applications. For the correct interpretation of the representative mark of diversity, knowledge of defect features and types of defects detected using radiographic casting inspection is necessary. Therefore, the expert knowledge is needed to solve complex problem of radiographic casting image interpretation. [3] M any researchers have worked on the segmentation of defects. The background subtraction and histogram threshold were performed to segment the defects from the background [4]. Due to that, there are various small background regions and noises besides the true defects, false detection will happen. National Seminar & Exhibition on Non-Destructive Evaluation, NDE 2014, Pune, December 4-6, 2014 (NDE-India 2014) Vol.20 No.6 (June 2015) - The e-Journal of Nondestructive Testing - ISSN 1435-4934 www.ndt.net/?id=17841

Transcript of Intelligent Fault Diagnosis System for Radiographic Images …...A variety of automated defect...

Page 1: Intelligent Fault Diagnosis System for Radiographic Images …...A variety of automated defect detection and classification systems have been applied extensively to wafer defects [7,8]

Intelligent Fault Diagnosis System for Radiographic Images of Valve Casting

using Adaptive Neuro Fuzzy Inference System

S.S.Khedkara, G.K.Awari

b, S.P.Untawale

c, S.P.Trikal

d

aYeshwantrao Chavan College of Engineering, Nagpur, Maharashtra, India

bTulshiram Gaikwad Patil College of Engineering, Nagpur, Maharashtra, India

cDatta Meghe Institute of Technology and Engineering Research, Wardha, Maharashtra, India

dShree Sant Gajanan Maharaj College of Enginering, Shegaon, Maharashtra, India

[email protected],

[email protected],

[email protected],

[email protected]

Abstract

This paper presents an automated defect detection and classification system for radiographic images of valve casting. The proposed system provides solution on complex problems of

radiographic image interpretation. The multiple morphological image processing implemented in

this work eliminates the problem of over-segmentation as well as gives good results to determine

shape and size of the flaws. The proposed system is a set of techniques dedicated to implement a

system of automatic inspection of radiographic images of valve casting.The steps involved in automated defect detection and classification are: digitization of the films, image pre-processing

directed mainly at the reduction/elimination of noise, contrast improvement and discriminate feature

enhancement facing the interpretation, multi-level morphological image processing & segmentation

of defect regions. Features are extracted from the segmented region and classification technique

such as adaptive neuro fuzzy inference system (ANFIS) is implemented. The proposed methodology was tested for various valve castings in a casting producing company.

Keywords: Radiographic Testing, Film Digitization, Image processing, Segmentation, ANFIS.

Introduction

Radioscopy rapidly became the accepted way for controlling the quality of die cast pieces. In

casting manufacturing, shrinkages occur during the cooling of the molten metal which can lead to

inhomogeneous regions within the work piece. These are noticeable, for example, by bubble-shaped

voids or fractures. Voids occur when the liquid metal fails to fill up the die completely or molten metal flows too slowly, whereas fractures are caused by mechanical stresses when neighboring

regions develop different temperature gradients. In addition, other casting defects can occur, such as

blow holes, inclusions etc. Ferrous alloy castings, such as valve body is considered to be part

relevant to operational safety. In order to ensure the safety of valve construction, the quality of valve

components is absolutely indispensable. Since last two decades, radiographic testing became the accepted method for the quality control of castings via visual or computer-aided analysis of X-ray

images [1]. The purpose of this non-destructive testing method is to locate casting defects which

may be located inside the piece and are thus not detectable to the naked eye [2] It is particularly

important for critical applications where valve casting failure can be resulted in to sudden terrible

disaster, such as in petrochemical refineries, oil and natural gas plants, and pneumatic applications. For the correct interpretation of the representative mark of diversity, knowledge of defect features

and types of defects detected using radiographic casting inspection is necessary. Therefore, the

expert knowledge is needed to solve complex problem of radiographic casting image interpretation.

[3] Many researchers have worked on the segmentation of defects. The background subtraction and

histogram threshold were performed to segment the defects from the background [4]. Due to that, there are various small background regions and noises besides the true defects, false detection will

happen.

National Seminar & Exhibition on Non-Destructive Evaluation, NDE 2014, Pune, December 4-6, 2014 (NDE-India 2014)

Vol.20 No.6 (June 2015) - The e-Journal of Nondestructive Testing - ISSN 1435-4934www.ndt.net/?id=17841

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Methods which include MONDAN-filter, signal synchronized filter does not work well on the segmentation of smaller defects, except the method of Computed Tomography (CT) [5]. Though

CT can detect the smaller defects in casting, it is not always feasible to industrial applications for its

expensive cost. An automated flaw detection method in aluminum castings based on the tracking of

potential defects in a radioscopic image sequence was presented by Mery, Filbert, [6] special

filtering and masking were used to segment the casting defects. However, the above research work was mainly done to detect the relative larger defects, so there are some limitations when using the

methods to detect the small defects in complex structure.

A variety of automated defect detection and classification systems have been applied extensively

to wafer defects [7,8] , Printed circuit board [9,10] Machine tools [11,12] Powder metallurgy

products [13] and gas pipelines [14]. It is noted that all the above methods for defect inspection are based on a very clean template or ruled geometry. If there are already non-uniform noises on the

radiographic image, it is difficult to identify the defect.

The image processing algorithm is a key to the automated defect detection and classification

systems. There are many basic theories, such as auto threshold selection [15], edge detection [16,17]

noise reduction [18], mask convolution, etc. It is generally called image pre-processing, which can be used to remove unwanted noises and enhance some important image features for further image

processing.

In the present research work, an integrated system consisting of an image processing and soft

computing techniques is designed and developed for casting defect detection and classification. The

effectiveness of the proposed system is evaluated by using an image database of real valve casting radiographs. The defects examined include gas inclusion, porosity, shrinkage, and tear, from

radiographic images of the valve casting. A system of automatic inspection of radiographic images

of valve casting is done in the following stages: i) film digitization, ii)image pre-processing with the

elimination of noise contrast improvement by histogram equalization and discriminate feature

enhancement, iii) multistage morphological image processing iv)a threshold based image segmentation v)Features Extraction and vi)classification through adaptive neuro fuzzy inference

system.

Experimentation

Digital image processing techniques are employed to reduce the noise effects and to improve the

contrast, so that the principal objects in the image become more apparent than the background.

Threshold selection methods, labeled techniques and feature extraction are used to obtain some

discriminatory features that can facilitate both the defect segmentation and the defect classification.

Figure 1 shows the major stages of automated defect detection and classification system. In order to improve the reliability of the results, various casting radiographs from Radiographic testing

department were used in a total of 56 films. These patterns have indications of the most frequent

classes of defects in valve casting, such as tear, porosity, gas inclusion, shrinkage etc. Automated

defect detection and classification is completed in the following sequences. Detail description for

each stage is presented in next subsections. Film Digitization. Radiographic films can be digitized by several systems. An overview of the

applicability of existing film digitization systems to non destructive testing is demonstrated by

Zscherpel. In present study, an Mirage II UMAX scanner was used. (Umax, 2009) (maximum

optical density: 3.3; maximum resolution for films: 2000 dpi) to scan the radiographic films. The

spatial resolution used in the study was 500 dpi (dots per inch), totaling an average image size of 2900 pixels (horizontal length), 950 pixels (vertical length), which resulted in an average pixel size

of 50 lm. Such resolution was adopted for the possibility of detecting and measuring defects of

hundredths of millimeters, which, in practical terms of radiographic inspection, is much higher than

the usual cases. The adopted gray scale resolution was 8 bits (256 levels), to match the best

resolution of the scanner. However, it is known that visually, the human being is not able to recognize any difference in gray scale over 128 levels and images of 10, 12 or 16 bits occupy a

significant amount of memory space.

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Image Pre-Processing. Radiographic films usually have noise and deficient contrast due to

intrinsic factors involved in the inspection technique, such as non-uniform illumination and the

limited range of intensities of the image capture device. Noise in scanned radiographic images is

usually characterized as randomly spread pixels, with intensity values that are different from their

neighboring pixels. Two preprocessing steps were carried out in this work: In the first step an adaptive Wiener filter and Gaussian low-pass filter were applied for reducing/eliminating noise, In

the second step contrast enhancement was applied for adjusting the image intensity values to a

specified range to contrast stretch.

The first algorithm implemented in the first preprocessing step uses a pixel-wise adaptive Wiener

method based on statistics estimated from a local neighborhood of each pixel. The Wiener method filters the image using neighborhoods to estimate the local image mean and standard deviation

through the local mean and variance around each pixel.

The next implemented process is a rotationally symmetric Gaussian low-pass filter with standard

deviation positive. Edges and other sharp transistorious in the gray levels of an image contribute

significantly to the high-frequency content of its Fourier transform.

Figure1: Method for automatic casting defect detection

The second step implements an algorithm to adjust image intensity values. Intensity values of each pixel of input image is mapped with new values of that of output image, so that values between that

representing the bottom 1% (0.01) and the top 1% (0.99) of the range are mapped to values between

[0 1]. Values below 0.01 and above 0.99 are clipped.

Multistage Morphological Image Processing. In this stage, multistage morphological image processing is applied on the pre-processed image to reduce the non uniform illumination.

Morphological operations apply a structuring element to an input image, creating an output image of

the same size. Two Morphological processing steps were carried out in this stage: in the first step,

combination of image erosion and image dilation with rectangular structuring element is applied for eliminating non uniform illumination. In the second step, same morphological combination with

another structuring element was applied for improving the results to a specified range. The number

of pixels added or removed from the objects in an image depends on the size and shape of the

structuring element used to process the image. A structuring element is a matrix consisting of only

0's and 1's that can have any arbitrary shape and size.

Image Segmentation. The defect region for each band must be isolated from the rest of the

elements. In this way, the scene of interest is framed for later analysis aimed at detecting possible

defects. The process is developed in three phases. The goal of the first phase is to find an optimal

overall threshold that can be used to convert the gray scale image into a binary image, separating an object’s pixels from the background pixels. The overall threshold is not suitable for the valve

casting images. It is due to the unavoidable image impurities. Keeping this in view, specific

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experimentation is carried out to find out the suitable threshold value for various types of casting defects. It is found that 0.32 is the best suitable threshold value. In next process, the connected

components in the binary image are labeled. The implemented algorithm uses the general procedure

proposed by Haralick and Shapiro.

To conclude, in final process, the maximum area is established. In this way, identification of

defects in the regions from among all the objects of the images is carried out. The results of applying this algorithm to radiographic image are shown in Table 1. Figures are to be referred in

sequence from 2-13.

Table 1: Progressive image processing results (a - l) of defect identification

a:

Original

Radiograph ( Colored Image)

b:

Gray Scale Image

c:

Gaussian Low pass filter and

Winner filter

Image

d:

Adaptive histogram Equalization output

e:

Contrast

Enhancement

f:

Morphological

Image processing (first stage)

-Image dilation

g:

Morphological Image processing

(first stage)

-Image erosion

h:

Morphological Image processing

(Second stage)

-Image dilation

i:

Morphological

Image processing (Second stage)

-Image erosion

j:

Image

Segmentation With 0.3 As

Experimental

Threshold Value.

k:

Filled Image

l:

Final Flaws Detection

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Features Extraction. For defect classification, the first stage is the features extraction. It is in terms of individual and overall characteristics of the heterogeneities. The output of this stage is a

description of each defect present in the image. This represents a great reduction in image

information from the original input image and ensures that the subsequent classification of defect

type and classification of the degree of acceptance are efficient.

In the present work, features describing the shape, size, location and intensity information of defect candidates were extracted. Features extracted are: area, equivalent diameter, eccentricity,

perimeter, solidity, extent, major axis length, minor axis length, orientation, euler number, centroid -

x coordinates and centroid - y coordinates). In this stage, the procedure generates an input vector

(for 12 components) for each defect region. Table 2 shows an example of input feature vector for

several defect candidates.

Table 2: Region Properties Defining Nature of Various Defects

Type of Defect

Pixel

Area

(a)

Equivalent

Diameter(b)

Eccentricity

(c)

Perimeter

(d)

Solidity

(e)

Extent

(f)

Gas Inclusion 71390.00 301.49 0.97 3542.11 0.61 0.37

Porosity 1183.00 38.81 0.44 153.84 0.91 0.72

Shrinkage 6485.00 90.87 0.88 436.41 0.86 0.61

Tear 1558.00 44.54 0.98 386.61 0.57 0.29

Non Defect 8.00 3.19 0.87 8.00 1.00 1.00

Type of Defect Major axis

Length(g)

Minor axis length (h)

Orientation (i)

Euler’s Number

(j)

Centroid-X

(k)

Centroid-Y

(l)

Gas Inclusion 789.88 193.40 85.62 1.00 63.42 307.32

Porosity 41.35 37.11 68.65 1.00 1723.74 492.21

Shrinkage 135.04 64.59 -12.21 1.00 2788.92 639.49

Tear 115.41 23.59 74.10 1.00 334.60 802.58

Non Defect 4.62 2.31 90.00 1.00 583.50 28.50

Adaptive Neuro Fuzzy Inference System. The approach to defect classification developed in

this stage is based on soft computing technique called adaptive neuro fuzzy inference system (ANFIS). In this stage, a set of 12 geometrical features which characterize the defect shape and

orientation was proposed and extracted between defect candidates. In further processing, the

validation of adaptive-network-based fuzzy inference system (ANFIS) for casting defect

classification is carried out. The automatic system of defect classification proposed consists in

detecting the four main types of valve casting defects met in practice plus the non-defect type. ANFIS has the basic structure of the classic fuzzy inference system. ANFIS classifier is a model

that maps input characteristics to input membership functions, input membership function to rules,

rules to a set of output characteristics, output characteristics to output membership functions, and

the output membership function to a single-valued output or a decision associated with the output.

The most frequently investigated ANFIS architecture is the first order Sugeno model, due to its efficiency and transparency. A representative ANFIS architecture with two inputs (x and y), one

output (f), and four rules is illustrated in Figure 18that consists of five layers: Adaptive

Fuzzification, Fuzzy Rule, Firing Strength Normalization, Adaptive Implication, and Output. Based

on the predefined ANFIS architecture, the modeling of a target system using the ANFIS is

implemented through its training process.

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Figure 2: ANFSI architecture Figure 3: Multi agent ANFIS system

To use the ANFIS as a defect classifier system, a schematic illustration is given in Figure 2. The inputs to the ANFIS are features extracted from previous stage (i.e. from feature extraction stage).

The output of the ANFIS is defined as 1 or -1 corresponding or not to a different defect class, as

required by the Sugeno model. The ANFIS model is optimized through its training process. Since

the architecture was pre-defined, the model is optimized only in the parameter.

The comprehensive classification process of the valve casting defects was automated through five independent ANFIS, one for each class of defect, non defect, Gas inclusion, Porosity,

Shrinkage and crack. Figure 3 indicates the Multi agent ANFIS system. All systems were first-order

Sugeno-type with 4 inputs and 3 bell membership functions per input. The neuro fuzzy structure of

ANFIS is shown in figure 4. The inputs to ANFIS represented some geometric features to determine

the type of defect for that vector, while its output was oriented to be 1 or -1 corresponding or not to the defect class. The fuzzy rules generated in the process are given in figure 5.

Figure 4: Neuro Fuzzy structure of ANFIS Figure 5: Fuzzy Rule generation

Results & Discussion

Image segmentation is applied on many X-ray images having various types of defects. The

results and subsequent feature extraction on few images are described below:

• All the areas of segmented image with defects can be observed clearly.

• In case of multiple flaws, defects are highlighted by the segmented image. Figure 6 shows

output of segmentation of radiographic images. First image shows actual digitalized

radiographs and adjacent image represents segmented regions.

• A comprehensive examination on the results obtained from image segmentation shows that

the multiple morphological image processing improves the defect detection.

• Different types of flaws with close contours are also identified properly.

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Figure 6: Output of Segmentation of Radiographic Images.

The inputs to ANFIS are four neighborhood attributes and the respective target for each will be a 5-element class vector.

• The accuracy of ANFIS classifier is measured by confusion matrix. It is plotted for all samples.

Tables 3 show confusion matrices consisting of the correct and incorrect classification.

Highlighted numbers indicate the correct classification and other values represent the misclassification.

Table 3: Confusion Matrix by ANFIS Classifier

Classified Defect

(Output)

Real Defect (Target)

Gas Inclusions Porosity Shrinkage Tear Non Defect

Gas Inclusions 03 00 00 00 00

Porosity 00 00 00 00 00

Shrinkage 01 00 19 01 01

Tear 00 00 01 04 00

Non Defect 00 01 04 00 10

Conclusion

It can be concluded that the proposed techniques are capable of achieving good results by

overcoming the complex problems of noise, low contrast and non uniform illumination of scanned

radiographic images. From the proposed research work, the following conclusions are drawn-

• Conventionally digital radiography method is universally being used for automated defect

detection and classification but it is too costly. The method used in this research work is based on

scanning of casting radiographs, is comparatively of low cost and proving to be best suitable for

small scale applications.

• Out of the available image pre-processing techniques, one of the techniques used in this research

work consists of combination of Gaussian low pass filter and adaptive winner filter. This

combination proved best suitable for casting defect detection.

• The multi-stage morphological image processing concept is found perfectly suitable for defect detection in casting.

• Experimental threshold value of 0.32 found most appropriate for threshold based image

segmentation as against the overall threshold value. This value is tested for 56 radiographic

images consisting of variety of defects.

• There is average 80 % accuracy of defect classification and 20% misclassification shown by

using Adaptive neuro fuzzy inference system.

Acknowledgment

The authors wish to acknowledge Mr.D.P.Sarma, President, Jayswal NECO group of Industries,

Nagpur, India, for providing radiographs of valve castings and giving permission to publish the

research work using the radiographic images.

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References [1] Merry D., Filbert D., Parspour N. (2000) “ Improvement in automated aluminum casting

inspection by finding correspondence of potential flaws in multiple radioscopic images”, in

proceedings of the 15th world conference on Non-destructive testing (WCNDT-2000), Rome,

Oct 15-21

[2] Silva R. R. da, Mery D.( 2004) “Accuracy estimation of detection of casting defects in X-ray images using some statistical techniques”. INSIGHT, J Br Inst Non-Destr Test ;49(10):603–

609.

[3] Juan Zapata, Rafael Vilar, Ramon Ruiz. (2011) “Performance evaluation of an automatic

inspection system of weld defects in radiographic images based on neuro-classifiers”.

ELSEVIER-Expert systems with Applications, 38, 8821-8824. [4] Kaftandjian V., Joly A., Odievre T., Courbiere M., Hantrais C.( 1998) “Automatic detection

and characterization of aluminum weld defects: comparison between radiography, radioscopy

and human interpretation”. In: Proceedings of the seventh European conference on

nondestructive testing, Copenhagen, Denmark. p. 1179–86.

[5] Mery D., Jaeger Th., Filbert D.( 2002) “A review of methods for automated recognition of casting defects”. INSIGHT, J Br Inst Non-Destr Test;44(7):428–36.

[6] Mery D., Filbert D.(2002) “Automated flaw detection in aluminum castings based on the

tracking of potential defects in a radioscopic image sequence”. IEEE Trans Rob

Autom;18(6):890–901.

[7] Maeda S., Kubota H., Makihira H., Ninomiya T., Nakagawa Y., Taniguchi Y.(1990) “Automated visual inspection for LSI wafer multilayer patterns by cascade pattern matching

algorithm”. Systems and Computers in Japan;21(12):65–77.

[8] Shapiro A. (1996) “Automatic classification of wafer defects: Status and industry needs”. In:

Proceedings of the IEEE/CPMT international electronics manufacturing technology (IEMT)

symposium; p.117–22. [9] Hara Y., Akiyama N., Karasaki K. (1983) “Automatic inspection system for printed circuit

boards”. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI);5(6):623–

30.

[10] Nagaswami V.R., Grimeault S., Dover R. J. (1997) “Application of segmented auto threshold

(SAT) for 0.5 mm and 0.35 mm logic interconnects layers”. IEEE/SEMI Advanced Semiconductor Manufacturing Conference and Workshop.

[11] Fernandez C., Fernandez S., Campoy P., Aracil R. (1994) “Online texture

analysis for flat products inspection. Neural nets implementation”. In: Proceedings of IECON;

p.867-72.

[12] Marino P., Dominguez M. A., Alonso M.(1999) “Machine-vision based detection for sheet metal industries”. In: Proceedings of IECON, San Jose, CA, USA. Institute of Electrical and

Electronics Engineers Computer Society;p. 1330–5.

[13] Kuang-Chao Fan, Shou-Hang Chen, Jhih-Yuan Chen, Wei-Bor Liao.(2010) “Development of

auto defect classification system on porosity powder metallurgy products” NDT and E

International; 43(4): 451–460. [14] Shafeek H. I., Gadelmawla E. S., Abdel-ShafyA. A., Elewa I. M. (2004) “Automatic inspection

of gas pipeline welding defects using an expert vision system”. NDT and E International;

37(4): 301–7.

[15] Otsu N. (1979), “A threshold selection method from gray-level histograms”. IEEE Transactions on Systems, Man and Cybernetics (SMC); 9(1):62–6.

[16] Canny J. (1986),“Computational approach to edge detection”. IEEE Transactions on Pattern

Analysis and Machine Intelligence (PAMI); 8(6): 679–98.

[17] Marr D., Poggio T. “Theory of edge detection”. Proceedings of Royal Society, London 1980;

B207:187–217. [18] Wu W. Y., Wang M. J. J., Liu C. M. (1992) “Performance evaluation of some noise reduction

methods”. CVGIP: Graph Models Image Process; 54(2):134–46.