KR101792564B1 - Image processing System and Image processing Method - Google Patents

Abstract

The present invention relates to an image processing method and an image processing apparatus using the same, and the image processing method according to an embodiment of the present invention includes an image capturing step of capturing an image with an electron microscope, Processing the image in one of the image tracking mode for tracking the target portion of the object or the image refinement mode for removing the noise of the still images photographed in the focused state of the target portion and refining the image, And an image output step for displaying the image.

Description

Technical Field [0001] The present invention relates to an image processing method and an image processing apparatus using the same,

The present invention relates to an image processing method and an image processing apparatus using the same, and more particularly, to an image processing method and an image processing apparatus using the same that can improve various noises generated in an image of a nanoparticle.

Recent advances in nanotechnology have greatly increased the demand for electron microscopes capable of imaging high magnification sample surfaces of 100,000 to 1 million times or more beyond the resolution limit of optical microscopes.

Patent Document 1 discloses a method and system for processing a moving image. In the case of Patent Document 1, processing of a moving image using image-based algorithms such as blurring, sharpening processing, contrast enhancement processing, and contour enhancement processing is disclosed . Patent Document 2 relates to a pattern image processing apparatus and relates to processing an image through digital conversion means, spatial filtering processing means, histogram processing means, threshold value setting means, image division and noise removal, pattern comparison and detection processing means Lt; / RTI >

However, in the case of Patent Documents 1 and 2, since the application object is limited to a hole pattern, it is difficult to apply it to various objects or samples.

Accordingly, there is an increasing need for an image processing method capable of providing a high-resolution image and an image processing apparatus using the same, which can be applied to various objects or samples.

KR 10-1552475 B1 KR 10-0264338 B1

An object of the present invention is to provide an image processing method capable of displaying noise in a nanoparticle image obtained using an electron microscope and displaying the same in real time, and an image processing apparatus using the same.

It is another object of the present invention to provide an image processing apparatus and an image processing method, which are capable of performing a high-speed scanning of a stage on which an object to be measured is to be measured in an electron microscope, An image processing method capable of providing an image and an image processing apparatus using the same.

It is another object of the present invention to provide an image processing method capable of providing a real-time nano image having high resolution while improving noise through a real-time image processing technique in a still-state nano image, and an image processing apparatus using the same.

It is another object of the present invention to provide an image processing method and an image processing apparatus using the image processing method, which can provide a clear and clean real time image by applying an image processing based filter to a noise image that can be generated by a high-speed scan.

An image processing method according to an embodiment of the present invention includes an image capturing step of capturing an image with an electron microscope, a image tracking mode of tracking a target portion of the object while scanning the measurement stage of the electron microscope, Processing the image in one of the image refinement modes for removing the noise of the still images photographed in the state and refining the image, and displaying the processed image.

The step of processing the image in the image tracking mode may include converting the photographed image data into image data of a predetermined color space, applying a Gaussian filter to the converted image data, And blending the image data to which the filter is applied and the image data in which the boundary portion is extracted.

Wherein the step of extracting the boundary part is performed by applying a Sobel filter, and in the blending step, a weight is applied to the image data to which the Sobel filter is applied according to a moving direction of the measurement stage, You can blend with images.

The step of processing the image in the image refinement mode may include converting the photographed image data into image data of a predetermined color space, removing image data whose error value is greater than a predetermined threshold value among the converted image data A step of filtering the image data from which the error has been eliminated to take a maximum value or a minimum value, adjusting the scale of the image through reduction and enlargement of the image data filtered by the maximum value or the minimum value, Calculating a difference value between the image filtered by the minimum value and the adjusted image, and calculating an output image according to the difference value.

Wherein the step of calculating the output image comprises the steps of: obtaining a difference value within a pixel area of a predetermined size between the filtered image and the adjusted image and comparing the difference value; An output image is calculated by applying an average value of the filtered image and the scaled image, and the filtered image may be calculated as an output image when the difference value is smaller than the predetermined threshold value.

Wherein the processing of the image further comprises processing an image in a start point correction mode, wherein the start point correction mode comprises the steps of: converting photographed image data into image data of a predetermined color space; A step of applying a filter, a step of binarizing the image data to which the Gaussian filter is applied, a step of extracting a boundary from the binarized image data, a step of extracting a feature from the extracted image data, For example.

The step of converting the image data into the predetermined color space may convert the photographed image data into grayscale image data.

An image processing apparatus according to another embodiment of the present invention includes an image capturing unit, an image conversion unit for converting the image data photographed by the image capturing unit into a predetermined color space, An image refinement unit for eliminating noise of still images photographed in a state in which the target portion is focused on the basis of the converted image data and refining the image based on the converted image data, And an image output unit for displaying an image processed by the image tracking unit or the image refinement unit.

The image tracking unit may further include a second image filter unit for applying a Gaussian filter to the converted image data, an edge extracting unit for extracting a boundary portion of the image data output from the second image filter unit, And an image blending unit for blending the image data output from the edge extracting unit.

The image refinement unit may further include an error calculation unit for eliminating image data whose error value is larger than a preset threshold value among the converted image data, a maximum value or a minimum value of the image data output from the error calculation unit An image scale adjusting unit for reducing and enlarging the image data output from the first image filter unit, and a second image filter unit for filtering the image data output from the scale adjusting unit and the first image filter unit based on the difference value And an image comparison and selection unit that selects an average value of the image data output from the scale adjustment unit and the first image filter unit or image data output from the first image filter unit as an output image.

And a start point correction unit for processing an image in the start point correction mode, wherein the start point correction unit comprises: a third image filter unit for applying a Gaussian filter to the converted image data; An edge extracting unit for extracting a boundary from the image data output from the image binarizing unit and a feature extracting unit for extracting a feature from the image data output from the edge extracting unit, And a feature extracting unit.

According to an embodiment of the present invention, there is provided an image processing method and an image processing apparatus using the same, which can improve the noise generated in a nanoparticle image acquired using an electron microscope and display the same in real time.

In addition, according to an embodiment of the present invention, there is provided an image processing method including a video image tracking mode for scanning a stage on which an object to be measured is placed at a high speed and searching for a target portion, It is possible to provide an image processing method capable of providing a clear and clean image with an image quality and an image processing apparatus using the same.

According to an embodiment of the present invention, there is provided an image processing method capable of providing a real-time nano image having high resolution while improving noise through a real-time image processing technique in a stationary nano image, and an image processing apparatus using the same .

According to an embodiment of the present invention, there is provided an image processing method capable of providing a clear and clean real-time image by applying an image processing-based filter to a noise image that can be generated by a high-speed scan, and an image processing apparatus using the same can do.

According to an embodiment of the present invention, noise of an image including angular noise including a shaken image, speckle noise, and the like acquired through an electron microscope using an image-based algorithm is improved, And can provide a clean image, and an image processing apparatus using the same.

1 is a flowchart schematically showing an image processing method according to an embodiment of the present invention.
2 is a flowchart schematically showing an image processing method according to another embodiment of the present invention.
3 is a block diagram schematically showing an image processing apparatus according to an embodiment of the present invention.
4 is a block diagram schematically showing an image processing apparatus according to another embodiment of the present invention.
FIG. 5 is a photograph illustrating a process of a Gaussian filtering step in an image processing method according to an embodiment of the present invention.
6 is a photograph illustrating a process of an edge extracting step in an image processing method according to an embodiment of the present invention.
FIG. 7 is a photograph illustrating a process of the image blending step in the image processing method according to an embodiment of the present invention.
FIG. 8 is a photograph showing a processing procedure of an error calculating step in the image processing method according to an embodiment of the present invention.
9 is a photograph illustrating a process of comparing and selecting images in the image processing method according to an embodiment of the present invention.

These embodiments are capable of various modifications and various embodiments, and specific embodiments will be described in detail with reference to the drawings. It is to be understood, however, that it is not intended to limit the scope of the specific embodiments but includes all transformations, equivalents, and alternatives falling within the spirit and scope of the disclosure disclosed. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS In the following description of the embodiments of the present invention,

In the embodiment, 'unit' or 'sub' performs at least one function or operation, and may be implemented in hardware or software, or a combination of hardware and software. In addition, a plurality of 'units' or a plurality of 'parts' are integrated into at least one module except for 'unit' or 'part' which need to be implemented by specific hardware, and are implemented by at least one processor (not shown) .

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. Referring to the accompanying drawings, the same or corresponding elements are denoted by the same reference numerals, and a duplicate description thereof will be omitted.

1 is a flowchart schematically showing an image processing method according to an embodiment of the present invention.

Referring to FIG. 1, an image processing method according to an exemplary embodiment includes selecting an image processing mode (S1), a video tracking mode, or an image processing mode (S2) And a video output step S5 of processing the tracking step S3 or the video finishing step S4 and outputting the processed video image.

In the imaging step S1, an electron microscope may be used. By placing an object to be photographed by an electron microscope on a measurement stage and photographing the image.

Specifically, in the image capturing step S1, the target is placed on a measurement stage of an electron microscope and the target is scanned to a low-magnification image while moving the stage. Then, the target portion to be measured is magnified at a high magnification, Can be obtained.

According to an embodiment of the present invention, a real-time nano-image can be provided by performing image processing while capturing an image, but the real-time nano-image may not necessarily be provided but may be used for processing a previously measured image.

In the mode selection step S2, one of the image tracking mode and the image refinement mode may be selected. By acquiring an image with a high-speed scan of the measurement stage in the image tracking mode, the target portion can be tracked in the acquired image. In the image tracking mode, in order to alleviate the motion blur phenomenon of the image, a series of boundary portions are emphasized and image blurring is performed to remove noise, and at the same time, have.

On the other hand, in the image refinement mode, it is possible to provide a still image in a state in which the target portion is focused, and it is possible to provide a refined image with improved image quality by mainly removing speckle noise.

The mode selection step S2 may be performed according to a user's selection or predetermined order and time. According to an embodiment, when the user moves the measurement stage in the electron microscope, the image tracking mode is used while the desired target portion is searched. When the target portion is determined, if the image refinement mode is input to confirm the target image, A control unit (not shown) may perform image processing by selecting a video image tracking mode or an image detailing mode according to a user input.

If the image tracking mode is selected, the image tracking step S3 may be performed. The electron microscope acquires images by detecting the electron energy generated by the electron gun and reflected back to the object. At this time, speckle noise occurs because the energy of the reflected electrons is not always constant. In addition, when an image is measured in the image tracking mode, since the image is captured while moving in the measurement stage, a distortion phenomenon (motion blur phenomenon) different from the structure of the actual object may occur.

According to an embodiment of the present invention, the image tracking step S3 may include an image conversion step S31, a Gaussian filtering step S33, and an image blurring step S37.

Also, according to an embodiment of the present invention, an edge extraction step S35 may be further included between the Gaussian filtering step S33 and the image blurring step S37. In the edge extracting step (S35), a process for sharpening an image can be performed. Since the image can be confirmed more accurately in the image refinement step, it can be selectively performed considering an image processing time and the like.

In order to remove such noise, it is converted into a predetermined color space of the measured image data input in the image conversion step S31. According to an embodiment, the RGB data of the measured image can be converted into the gray scale image data.

Then, the Gaussian filter is applied to the image data of the gray scale converted in the Gaussian filtering step (S33), and the speckle noise can be removed.

FIG. 5 is a photograph illustrating a process of a Gaussian filtering step in an image processing method according to an embodiment of the present invention. Fig. 5 shows images before (Fig. 5 (a)) and after (Fig. 5 (b)) of the Gaussian filter for the gray scale image. The blurring of the image can be performed by applying a Gaussian filter, which is a low-pass filter for eliminating high frequency components of the image. Referring to FIG. 5, bright or dark dots appear scattered in the image before application of the Gaussian filter (FIG. 5 (a)), but in the case of the application (FIG. 5 (b) Most of the noise is removed.

Then, an edge is extracted from the image in the edge extracting step (S35), and an unclear boundary portion due to the motion blur phenomenon can be extracted and emphasized. Image Sharpening, Unsharp Masking Processing, Edge Enhancement, and the like can be performed in order to improve the distortion phenomenon such as motion blur due to the movement of the stage. Throughout the process, the outline or boundary of an object can be emphasized.

According to an embodiment of the present invention, a Sobel filter may be applied in the edge extraction step S35. By applying the Sobel filter, it is possible to extract and emphasize the unclear boundary due to the motion blur phenomenon and to emphasize the boundary to prevent the blurring of the boundary due to the Gaussian filter. More specifically, the Sobel filter may be applied to the x-axis, the Sobel filter may be applied to the y-axis, and the weights in the subsequent image blending step (S37) may be applied using the results of the x-axis and y, respectively.

6 is a graph illustrating a result obtained by applying a Sobel filter (FIG. 6A) on the x-axis and a Sobel filter applied on the y-axis in the edge extraction step S35 in the image processing method according to an embodiment of the present invention 6 (b)). It can be seen that a clearer image is provided by applying the Sobel filter to the x-axis, i.e., by edge extraction on the x-axis, and this result can then be used in the image blending step (S37).

The image blending step S37 is performed by blending the image data to which the Gaussian filter of the image is applied and the image data to which the edge extraction is applied. According to one embodiment, the image data to which the Gaussian filter is applied and the image data to which the Sobel filter is applied may be blended, and the weight may be set to be different according to the moving direction of the stage.

FIG. 7 is a photograph showing a process of the image blending step (S37) in the image processing method according to an embodiment of the present invention. 7A shows an original image. FIG. 7B shows an example in which the x-axis blending weight is set to 1 and the y-axis blending weight is set to 0 in the image data to which the Gaussian filter is applied and the image data to which the Sobel filter is applied And shows the blended image. FIG. 7C shows an image blended by processing the Gaussian filter-applied image data and the Sobel filter-applied image data by processing an x-axis blending weight value of 0.5 and a y-axis blending weight value of 0.5. FIG. 7 (d) shows an image obtained by blending the x-axis blending weight with 0 and the y-axis blending weight with 1 in the image data to which the Gaussian filter is applied and the image data to which the Sobel filter is applied.

Referring to FIGS. 7 (b) to 7 (d), it can be seen that a clearer and cleaner image is obtained as the weights are set larger in the x-axis blending. That is, in the edge extracting step S35 in FIG. 6, since a clearer image can be obtained than the result of applying the Sobel filter to the x-axis, a clearer image can be obtained when a larger weight is applied to the x-axis blending Able to know.

 The image blending result can be confirmed by the user through the image output step S5. The user can view the image in real time by processing the image in the image tracking mode while moving the stage. The image tracking mode may be maintained for a predetermined time, and the controller may control the start time and end time of the image to control the duration of the image tracking mode.

After the predetermined time or when the target portion is determined according to the user's input, the image processing can be performed in the image refinement mode.

In the image refinement mode, the refined image of the target portion can be obtained. After the target part is confirmed, the image can be captured in the stopped state. The image obtained in this way does not cause a lot of distortion (motion blur) due to the movement of the measurement stage, but the speckle noise such as black or white spots mainly appears due to the characteristics of the electron microscope.

In the image refinement mode, to improve the speckle noise in the acquired image, the multiple images acquired at the same position are first checked for the same position, and then subjected to the image selection process. Then, The maximum value or the minimum value is taken, and the noise is effectively improved by a series of processes in which the optimum value is derived through comparison with the blurred image through the scale adjustment process.

In the image refinement step S4, the image transformation step S40, the error calculation step S41, the maximum value or minimum value filtering step S45, the image scale adjustment step S45 and the image comparison and selection step S47 are performed. . ≪ / RTI >

In the image conversion step S40, as in the image conversion step S31 of the image tracking step S3, the color information of the image can be converted into data of the color space for image processing. According to one embodiment, data in the RGB color space can be converted to gray scale data.

Then, in the error calculation step (S41), the error with the previous image is compared, and an image with a large error can be excluded from the processing object. In the error calculation step S41, it is checked whether the images measured in the image capturing step are images at the same position.

According to an embodiment, if the difference image between the obtained images is obtained, and if the same portion is dark and the different portion is displayed as a white image, the brightness values of all of the difference images are all added, If the threshold value is equal to or greater than a predetermined threshold value, the corresponding image may be excluded.

FIG. 8 is a photograph showing a processing procedure of an error calculating step S41 in the image processing method according to an embodiment of the present invention.

8 (a), 8 (b), and 8 (c) are images at different viewpoints. 8 (d) is a difference image between the image of FIG. 8 (a) and the image of FIG. 8 (c), and FIG. 8 (e) is the difference image of FIG. 8 (b) and FIG. Here, the same part is displayed in black and the other part is displayed in white. In this case, in the case of the difference image of FIG. 8 (d) in which most of the pixels are displayed in black, the sum of the brightness values does not exceed the preset limit value, but in the case of the image of FIG. 8 (e) Exceeds the preset limit value. 8 (a) and 8 (c) have a small amount of error, but the image of FIG. 8 (b) has a large error.

After the image having a large error is removed, filtering may be performed to take the maximum value or the minimum value among the pixels of the image through the maximum value or minimum value filtering step S43. Depending on the input of the user, it is possible to select whether to perform the maximum value filtering or the minimum value filtering in the control unit, but the maximum value or the minimum value filtering can be performed according to preset conditions, though not necessarily limited thereto.

After applying the maximum value or minimum value filtering step S43, noise can be minimized through the image scale adjustment step S45. Specifically, it is possible to perform image scale down and scale up adjustment using a pyramid algorithm on an image to which a maximum / minimum filter is applied. According to an embodiment, the image of the previous process may be reduced to 1/256 magnification and then converted to the original size. The reconstructed image converted to its original size has a blur effect, but it can remove speckle noise from the image.

The image generated through the reduction and enlargement of the image scale in step S45 and the image on which the maximum / minimum filter is applied in step S43 are compared with each other in the image comparison and selection step S47 , One of two images can be selected and processed.

Specifically, when the pixel values of the two images are compared and the average value of the adjacent pixels is used when the difference value is larger than the predetermined value, and when the difference value is smaller than the preset value, the maximum / Can be used as it is.

If the difference is large, it is likely to be a speckle noise. To improve this, an average value of adjacent pixels is applied.

According to an embodiment of the present invention, when the average value of adjacent pixels is obtained, the size of the adjacent pixel region may be set in advance according to a user's input. For example, 3x3, 5x5, 7x7, or 9x 9. But may be variously set according to the image processing condition and the environment of the image processing apparatus.

9 is a photograph illustrating a process of comparing and selecting an image (S47) in the image processing method according to an embodiment of the present invention.

In the case of FIG. 9A, the maximum value filter is applied after the step S43, and FIG. 9B is the image after applying the pyramid algorithm after the step S45. When the pixel values in the images of FIGS. 9A and 9B are compared with each other, the image is larger than a preset value, and an average value is applied to obtain an image as shown in FIG. 9C. As can be seen from FIG. 9 (c), it can be seen that black and white spots are removed when compared with the maximum value filter image (FIG. 9 (a)).

Then, the processed image data through the image refinement step S4 may be provided to the image output step S5.

2 is a flowchart schematically showing an image processing method according to another embodiment of the present invention.

Referring to FIG. 2, the start point correction mode may be further included in the processing of the image photographed in the image capturing step S1. The image tracking step S3 and the image refinement step S4 may be performed as in the embodiment of FIG.

In the start point correction mode, a line indicating the position of the distorted image with respect to the image distortion caused by the movement of the case stage can be displayed to the user.

In the case of the starting point correcting step for performing the starting point correcting mode, the image converting step S61, the Gaussian filtering step S63, the image binarizing step S65, the edge extracting step S67 and the feature extracting step S69 .

Specifically, in the image conversion step S61, the RGB color data of the measured image is converted into gray scale data. After applying the Gaussian filter, the image binarization step S65 may be performed by applying the Otsu thresholding method. Then, an edge extraction step (S67) is performed by applying a Canny edge detector, a feature is extracted using a hough transform, and a line is drawn to display a starting point of an area without image distortion . By providing such information, the user can more easily detect the image distortion position due to the fast scanning.

 3 is a block diagram schematically showing an image processing apparatus according to an embodiment of the present invention.

3, an image processing apparatus according to an embodiment of the present invention includes an image capturing unit 10, an image conversion unit 20 connected thereto, a refinement unit 30 connected to the image conversion unit 20, A tracking unit 40 and an image output unit 40 connected to the image refinement unit 30 and the image tracking unit 40. [ In the case of the image processing apparatus of the present invention, the methods used in the image processing method can be applied.

Specifically, in the image capturing unit 10, an image of a target object can be photographed by an electron microscope.

The image photographed by the image capturing unit 10 may be converted to gray scale through the image converting unit 20 and then input to the image refining unit 30 or the image capturing unit 40. [ The image data processed by the image refinement unit 30 or the image tracking unit 40 can provide a nano image in real time through the image output unit 50. According to one embodiment, the display unit 50 may be applied to various display devices that can be used in the art.

A second image filter unit 41 for applying a Gaussian filter to apply blurring to the converted image data in the case of the image tracking unit 40, and a second image filter unit 41 for extracting a boundary portion using an Sobel filter in an image to which a Gaussian filter is applied An edge extracting unit 43 and an image blending unit 45 for blending the image data output from the edge extracting unit 43 and the image data output from the second image filter unit 41. [

The edge extracting unit 43 may be selectively provided to reduce image processing time and provide high-speed real-time scanning for sharpening an image.

 On the other hand, the refinement unit 30 is for refining the image of the target part to be acquired, and may be performed after tracking the target part through the image tracking unit 40, or when the target part is predetermined, 30). ≪ / RTI >

On the other hand, the precision unit 30 includes an error calculator 31 for excluding an image having a large error in data converted into gray scale through the image conversion unit 20, and a filtering unit 31 for filtering the maximum or minimum value among the pixels A first image filter unit 33, an image scale adjustment unit 35 and a first image filter unit 33 for adjusting an image scale through image enlargement after image reduction using a pyramid algorithm, The image data of the first image filter unit 35 and the image data of the image scale adjustment unit 35 are compared. When the comparison value is larger than the preset value, the average value of the adjacent pixels is applied. If the comparison value is smaller than the preset value, 33).

4 is a block diagram schematically showing an image processing apparatus according to another embodiment of the present invention.

Referring to FIG. 4, the image processing apparatus according to an embodiment of the present invention further includes a start point correction unit 60. The starting point correction unit 60 includes a third image filter unit 61 for receiving the image data converted into gray scale and applying a Gaussian filter, an image binarizer 63 for binarizing the image by applying a perceptual aliasing method, An edge extracting unit 65 for extracting a boundary by applying a canyon edge tracker, and a feature extracting unit 67 for extracting a feature and displaying a starting point of an area having no image distortion.

The video data processed by the starting point correction unit 60 may be provided to the user through the video output unit 50. [

According to an image processing method and an image processing apparatus according to an embodiment of the present invention, a nanoparticle image can be obtained through an electron microscope in a image tracking mode (or a image tracking unit) and an image refinement mode (or an image refinement unit) , It is possible to provide a clear image by improving the noise in the image, and to provide a real-time image.

The image obtained in the image tracking mode (or the image tracking unit) may be distorted due to noise such as motion blur added to the image obtained during the movement in the measurement stage. However, after the color information is converted into gray scale data for the image data, the spatial domain filter processing for removing the speckle noise is performed, and the boundary portion is detected and extracted to correct the image distortion that may occur due to the motion Noise can be improved by coexisting with the image subjected to the spatial domain filter processing.

In addition, in the case of a plurality of images captured by the image refinement mode (or the image refinement unit), color information is converted into gray scale data, and then an image in which a positional error occurs in a plurality of still images is identified and selected, A maximum value or a minimum value filter is applied to overcome the same speckle noise except for an error image and the maximum value or minimum value filter is superimposed and applied, It is possible to derive an image having the optimum value by comparing the image data.

Accordingly, it is possible to provide a clear and clean image with no distorted image or speckle noise acquired by an electron microscope.

Claims (11)

An image photographing step of photographing an image with an electron microscope;
In the image tracking mode in which the target portion of the object is tracked while scanning the measurement stage of the electron microscope or the image refinement mode in which the noise is removed from the still images photographed in the focused state of the target portion and the image is refined, Lt; / RTI > And
And a video output step of displaying the processed video,
Wherein the processing the image in the image refinement mode comprises:
Converting the photographed image data into image data of a predetermined color space;
Removing image data whose error value is greater than a predetermined threshold value among the converted image data;
Filtering out the error-eliminated image data to take a maximum value or a minimum value;
Adjusting a scale of the image through reduction and enlargement of the image data filtered by the maximum value or the minimum value; And
Calculating a difference value between the image filtered by the maximum or minimum value and the adjusted image and calculating an output image according to the difference value.
The method according to claim 1,
Wherein the processing the image in the image tracking mode comprises:
Converting the photographed image data into image data of a predetermined color space;
Applying a Gaussian filter to the transformed image data;
Extracting a boundary portion from the image data to which the filter is applied; And
And blending the image data to which the filter is applied and the image data in which the boundary portion is extracted.
3. The method of claim 2,
The step of extracting the boundary portion is performed by applying a Sobel filter,
Wherein in the blending step, a weight is applied to the image data to which the Sobel filter is applied according to a moving direction of the measurement stage, and the image is blended with the image to which the Gaussian filter is applied.
delete The method according to claim 1,
Wherein the step of calculating the output image comprises:
Calculating a difference value within a pixel region of a predetermined size between the filtered image and the scaled image,
Calculating an output image by applying an average value of the filtered image and the scaled image if the difference value is larger than a predetermined threshold value,
And calculating the filtered image as an output image when the difference value is smaller than the predetermined threshold value.
The method according to claim 1,
Wherein the step of processing the image further comprises processing an image in a start point correction mode,
Converting the photographed image data into image data of a predetermined color space;
Applying a Gaussian filter to the transformed image data;
Binarizing the image data to which the Gaussian filter is applied;
Extracting a boundary from the binarized image data; And
And extracting features from the extracted image data to display a starting point of an area free from image distortion.
The method according to claim 1,
Wherein the converting the image data into the predetermined color space comprises:
And converting the photographed image data into grayscale image data.
An image capturing unit;
An image conversion unit for converting the image data photographed by the image capturing unit into a predetermined color space;
A video tracking unit for tracking a target portion of the object while moving and scanning the measurement stage of the electron microscope based on the converted image data;
An image refinement unit for eliminating noise of still images photographed in a focused state on a target portion and refining an image based on the converted image data; And
And an image output unit for displaying an image processed by the image tracking unit or the image refinement unit,
Wherein the image refinement unit comprises:
An error calculator for removing image data having an error value greater than a predetermined threshold value among the converted image data;
A first image filtering unit for filtering the image data output from the error calculating unit to take a maximum value or a minimum value;
An image scale adjustment unit for reducing and enlarging the image data output from the first image filter unit; And
And an average value of the image data output from the scale adjusting unit and the first image filter unit or an average value of the image data output from the first image filter unit, And an image comparison and selection unit for selecting the image data as an output image.
9. The method of claim 8,
The image-
A second image filter for applying a Gaussian filter to the converted image data;
An edge extracting unit for extracting a boundary portion of the image data output from the second image filter unit; And
And an image blending unit for blending the image data output from the second image filter unit and the edge extraction unit.
delete 9. The method of claim 8,
And a start point correction unit for processing the image in the start point correction mode,
The starting point correction unit
A third image filter unit for applying a Gaussian filter to the converted image data;
An image binarizer for binarizing the image data output from the third image filter unit;
An edge extraction unit for extracting a boundary from the image data output from the image binarization unit; And
And a feature extracting unit that extracts a feature from the image data output from the edge extracting unit and displays a starting point of an area without image distortion.

Images