KR100485593B1 - A method for processing consecutive image input and a system thereof - Google Patents

Abstract

A method for processing images inputted continuously to frames and a system for the same are provided to remove effectively the noise in the photographed image and to output a clear image without damage of detail. A segmentation filter(201) divides an image into a segment constructed by a predetermined number of pixels and classifies each segment into a low or a high brightness region by using brightness of image data included to each segment. The first filter(202) removes the impulse noise from the image data included to one or more segment classified by the low brightness region. A motion detector(203) senses the motion pixel among the image data. The second filter(204) senses the false color noise among the image data on the basis of the sensed motion pixel and removes the sensed false color noise from the image data.

Description

Image processing method and system for continuous input image by frame {A METHOD FOR PROCESSING CONSECUTIVE IMAGE INPUT AND A SYSTEM THEREOF}

The present invention relates to an image processing method and system for an image that is continuously input by frame, and more particularly, to an image processing method and system that can effectively remove noise included in an image photographed in a dark environment. will be.

Today, with the development of video technology, video media capable of capturing and storing various images such as high performance cameras, digital cameras, CCTVs, and video capture systems are being developed.

The most important problem of such image capturing media is the accurate reconstruction of the image image of the photographed subject. In particular, noise that may be added to an image not only degrades the quality of the image but also reduces the compression efficiency.

Therefore, removing noise quickly and accurately is the most important problem for restoring the image image.

However, when shooting an image in a dark environment with low light, such as when a CCTV is installed inside a building where there is no lighting at night, it contains more noise and accurately expresses details than when shooting in an environment with sufficient light. There was a problem that it is difficult to get a good image.

In addition, when the noise is to be removed from the image photographed in a dark environment according to the prior art, there is a problem that the detail or the edge of the image itself is damaged and the sharpness of the image is reduced, resulting in a blurred image.

Therefore, it is necessary to remove noise from an image photographed in a dark environment and to obtain a clear image.

The present invention provides an image processing method and a system capable of outputting a clear image by effectively removing noise from the image while not damaging detail, even for an image that is taken in an environment in which light is not sufficient and contains a lot of noise. It aims to provide.

Also, the present invention divides an image into image data of a high brightness region and image data of a low brightness region to process only an image of a low brightness region that generates a lot of noise, thereby reducing the amount of computation and improving image processing efficiency. It is an object to provide a method and a system thereof.

In addition, the present invention processes the image according to the order of spatial filtering, motion detection, and temporal filtering, so that smearing to surrounding pixels generated by temporal filtering affects motion detection. It is an object of the present invention to provide an image processing method and a system in which more accurate motion detection can be performed.

In addition, an object of the present invention is to provide an image processing method and system that can perform more effective temporal filtering based on the accurate motion detection result as described above.

In addition, an object of the present invention is to provide an image processing method and system capable of preserving edges or details while effectively removing impulsive noise or Poisson noise in the course of performing spatial filtering.

In addition, the present invention provides an image processing method and system capable of effectively removing a false motion pixel mistaken as a motion pixel as a result of motion detection and reducing a smoothing phenomenon by dynamically determining a threshold for each frame.

In order to achieve the above object and to solve the problems of the prior art, the present invention provides an image processing method for an image that is continuously input for each frame, comprising: dividing the image into segments consisting of a predetermined number of pixels; A first step of dividing each segment into a low brightness region or a high brightness region by using brightness of image data included in each segment; A second step of removing impulsive noise from image data included in one or more segments divided into low brightness regions; Detecting a motion fixel among the image data; Detecting false color noise among the image data based on the detected motion pixel; And a fifth step of removing the sensed false color noise from the image data.

In addition, the image processing system continuously input for each frame according to the present invention divides the image into segments composed of a predetermined number of pixels, and each segment is divided into a low brightness region or a high brightness by using the brightness of the image data included in each segment. Segmentation filters divided into regions; A first filter for removing impulsive noise from image data included in one or more segments divided into low brightness regions; A motion detector for detecting a motion fixel among the image data; And a second filter configured to detect false color noise among the image data based on the detected motion pixel and to remove the detected false color noise from the image data.

Hereinafter, with reference to the accompanying drawings will be described an embodiment of the present invention;

Since the present invention can be used to effectively remove noise without damaging details and edges from an image taken in a "low light environment," the image taken in a dark environment will be described below. It mainly describes the process of processing. However, this is merely exemplary, and the present invention may be applied to all images taken in any environment such as a dark environment as well as an environment having sufficient light.

1 is a diagram illustrating a case where the image processing system according to the present invention is used as a prefilter. The image captured by the camera 101 is captured by the capture module 102, and the image processing system 103 acquires a high quality image and improves compression efficiency before the image is encoded by the video encoder, such as digitization or compression. Preprocessing is performed such as removing noise from the image.

The image encoded by video encoder 104 may be stored in a storage space of the system, or may be transmitted to a remote system via a network. An image stored in the storage space, an image transmitted to the remote system, or an image transmitted and stored to the remote system may be decoded by the video decoder 105 and displayed on a predetermined display means 106. Meanwhile, FIG. 1 illustrates a case in which the image processing system 103 according to the present invention is used as a prefilter, but this is merely an example, and the present invention effectively removes noise from an image captured by a photographing apparatus and improves sharpness. It can be widely used to obtain an image that can be enhanced.

Hereinafter, an operation of the image processing system 103 or 200 according to an embodiment of the present invention will be described in more detail with reference to FIG. 2. 2 is a block diagram showing an image processing system 200 according to the present embodiment.

The image processing system 200 includes a segmentation filter 201, a first filter 202, a motion detector 203, and a second filter 204.

The segmentation filter 201 divides an image sequentially inputted for each frame into segments composed of a predetermined number of pixels, and divides each segment into a low light region or a high level using brightness of image data included in each segment. It is divided into a high light region. Contrast, brightness, or focus measure values of each pixel may be used to determine brightness.

The first filter 202 removes impulsive noise from image data included in one or more segments divided into low brightness regions. In the present specification, "image data of an area including segments divided into low-lightness areas" may be briefly expressed as "image data", and also clearly distinguished from image data belonging to a low-brightness area or a high-brightness area, respectively. For example, an image of an area including both a low brightness area and a high brightness area, that is, an image input to the image processing system 200 may be expressed as a “full image”. Thus, the image data may be part or all of the entire image.

The motion detector 203 detects a motion fixel among the image data.

The second filter 204 senses false color noise and removes the sensed false color noise from the image data.

Hereinafter, the operation of each component of the image processing system 200 as shown in FIG. 2 will be described in more detail with reference to FIG. 3.

When the image is sequentially input for each frame, the segmentation filter 201 divides the entire image into segments composed of a predetermined number of pixels (S301).

For example, an image captured from the camera 101 may be input to the image processing system 200 at a frame rate of 15 [frame / sec].

Whether the segmentation filter 201 divides the entire image into segments having a size may be selected in consideration of processing efficiency and accuracy, and may be divided into segments having a size of 4 pixels × 4 pixels, for example. Can be.

Segmentation filter 201 recognizes the brightness of the image data included in each segment, the high light region when the brightness exceeds the threshold, and low light region when the brightness is below the threshold (S302).

4 is a diagram illustrating an entire image divided into a high brightness region and a low brightness region by the segmentation filter 201. Reference numeral 401 denotes an entire image, and reference numeral 402 denotes an image divided into a high brightness region and a low brightness region. In reference numeral 402, a white portion is a low brightness region and a black portion is a high brightness region. , Is reversed.

At this time, the image processing system 200 skips image processing on image data included in segments divided into high brightness areas, and performs image processing such as noise reduction only on image data included in segments divided into low brightness areas. The computational efficiency of the overall image processing can be improved. That is, according to the present invention, since the image area to be processed by the image processing system 200 is reduced, the noise is sufficiently removed by processing only image data of a dark area which is a noisy area while reducing the time required for calculation. You can also ensure that. In particular, according to the present exemplary embodiment, the image processing of the high-brightness region can be prevented from being blurred due to the application of the noise filter by omitting image processing for the high-brightness region.

The first filter 202 divides the image data included in the segment divided into the low brightness region in the segmentation filter 201 into one or more unit regions, and includes pixels including impulsive noise in each unit region, that is, impulsive noise. It is detected whether there is a corroded pixel (S303). In the present specification, a pixel in which impulsive noise occurs is referred to as a "first pixel".

5 is a diagram showing impulsive noise or Poisson noise as described later. Reference numeral 501 in FIG. 5A denotes a first pixel in which impulsive noise is generated. 5B illustrates each pixel value of pixels belonging to a line including the first pixel 501. Reference numeral 502 indicates that the pixel value at the first pixel has an abnormally large value, that is, impulsive noise has occurred.

In order to detect the first pixel, the first filter 202 measures whether or not the center pixel is the first pixel by measuring a similarity between the center pixel in the unit region and the peripheral pixel of the center pixel. In this case, a threshold such as Equation 1 may be used to determine the similarity.

remind Is the minimum threshold for sensing pixels that contain impulsive noise, and Is an average value of each pixel included in the unit area. The first filter 202 determines that the predetermined pixel is the first pixel when the predetermined pixel has a higher value than the threshold value calculated by Equation 1, and when the predetermined pixel has a lower value than the threshold, impulse noise is determined. It is judged that the pixel which did not occur.

When the pixel is detected as the first pixel, the first filter 202 applies a median filter, such as Equation 2, to the first pixel to replace the value of the first pixel with the median value, thereby eliminating impulsive noise. Remove

In Equation 2, W denotes a unit region, and (i, j) denotes coordinates of each pixel in the unit region. Y means the changed value of the first pixel, that is, the impulsive noise has been removed.

However, Poisson noise is often generated in the peripheral pixels of the pixel in which the impulsive noise occurs. Reference numeral 503 in FIG. 5A denotes pixels in which Poisson noise has been generated. As shown by reference numeral 504 in FIG. 5B, Poisson noise occurs with a distribution similar to the value located at the tail portion of the Gaussian distribution.

One method of removing Poisson noise may be a method of approximating a Poisson noise distribution to a Gaussian distribution having a mean and a variation, and then removing the Poisson noise.

Therefore, the first filter 202 applies a mean-variance filter to the second pixels adjacent to the first pixel to remove such Poisson noise, thereby surrounding the pixels including the impulsive noise. The values of the pixels LND are corrected (S305). For example, the Min-Vance filter may be applied to peripheral pixels, that is, eight pixels, within an area having a size of 3 pixels by 3 pixels about the first pixel.

Equation 3 shows a Min-Vance filter according to the present invention.

Wow Is the coordinate of the first pixel Represents the variance and mean of the second pixels of a region of a predetermined predetermined size of.

In an area of a certain size without change Is calculated, and Equation 3 can be expressed as follows.

Equation 4 means that the pixel value of the surrounding pixel is close to the average value in that region, and Poisson noise is effectively averaged (ie, removed).

Conversely, in the area with edges or details in the image data, Since E has a large value, Equation 3 can be approximated as follows.

As can be seen from Equation 5, the edge or detail is preserved because the second pixel value is maintained in the region including the edge or the detail, that is, the region with much change.

According to the above configuration, the first filter 202 removes the impulsive noise by applying a median filter to the first pixel including the impulsive noise, and pushes the second pixel which is the peripheral pixel of the first pixel. -Apply a variance filter to remove Poisson noise.

Since the first filter 202 applies the min-variance filter to the second pixel around the first pixel only when the first pixel is detected, consequently the median filter for removing impulsive noise according to the present invention. Min-Verence filters work together to remove the Poisson noise.

In particular, the min-variance filter according to the present invention can preserve edges or details in that it replaces the second pixel around the first pixel with a min value only in an area without edges or details.

The motion detector 203 senses a motion pixel by comparing the image data continuously input in units of frames for each frame (S306). In the present specification, when image data corresponding to an arbitrary frame is referred to as "first image data", the image data corresponding to a frame input immediately after the frame is determined as "second image data".

Detecting the motion pixel (S306) is performed before removing the false color noise in the image data. Since filtering to remove false color noise may cause motion blur, it is preferable that false color noise be applied only to a static region in which no motion pixel exists.

In the case of false color noise, unlike impulsive noise or Gaussian noise, it is difficult to detect by comparing the pixel values of the corresponding pixel or neighboring pixels in an image of one frame. . Therefore, the previous frame of the current frame must be stored in a predetermined memory means for a certain period or more in order of time, that is, to compare successive frames with each other.

Therefore, when the pixel values of pixels corresponding to each other in a continuously input image change, it can be regarded as a normal motion pixel or undesirable false color noise, and once a motion pixel is detected, the rest is treated as false color noise. can do.

The motion detector 203 may detect a motion pixel by comparing pixels corresponding to each other in a predetermined unit region in a continuously input frame (S307). The unit area for motion detecting may be set differently from the unit area used to remove the impulsive noise or the Poisson noise as described above. In addition, since the motion detector 203 compares the image of the previous frame with the image of the current frame, the motion detector 203 should temporarily store the image corresponding to the previous frame.

The motion detector 203 according to the present exemplary embodiment may compare pixels in the same unit area on successive different frames by using the threshold value as shown in Equation (6).

The sum absolute difference ( SAD ) represents a threshold determined for each unit region (blocks (m, n)) for motion detection. x ( k, l, t- 1) represents the pixel value of the pixel in the previous frame, and x ( k, l, t ) represents the pixel value of the pixel corresponding to the pixel in the current frame. In addition, A _mn represents a unit region to which the image data of the frame belongs. Therefore, according to equation (6 ) , pixels having coordinates (k, l) are included in the unit area.

As described above, once a pixel that is a motion pixel is determined among pixels whose pixel value is changed between successive frames, the second filter 204 may be configured for a static region that does not include motion pixels. Temporal filtering can be applied to remove false color noise.

However, as shown in FIG. 6, the motion detector 203 may misjudge a pixel in which false color noise has occurred as a motion pixel. In this specification, a pixel falsely determined as a motion pixel as a pixel having false color noise is referred to as a "false motion pixel".

6 (a) and 6 (b) show regions corresponding to each other on consecutive frames. The motion detector 203 continuously recognizing (a) and (b) of FIG. 6 incorrectly determines that the pixels indicated by the reference numeral 601 and the reference numeral 602 are pixels in which the motion occurs even though the pixels with false color noise have occurred. can do.

In an image of successive frames, the false color pixels (including the false motion pixels) must be removed separately from the motion pixels. When using a low pass filter as a temporal filter as in the prior art, a problem may occur in which smoothing is excessive and details are not properly represented. Also, a low pass filter may be a false motion that is not judged as false color noise. There is a problem that a false motion pixel cannot be removed because the pixel cannot be distinguished from a normal motion pixel.

The second filter 204 according to the present embodiment dynamically determines thresholds on a frame-by-frame basis to correct false color pixels including false motion pixels and preserve image characteristics such as edges and details in dark areas. do.

Hereinafter, a configuration in which the second filter 204 removes false color noise will be described in detail. For convenience of description, "first image data" corresponding to a predetermined frame and image data corresponding to a frame input immediately after the frame are referred to as "second image data".

The second filter 204 converts the first image data to gray scale to generate first gray scale image data, and converts the second image data to gray scale to generate second gray scale image data.

The second filter 204 calculates an overall average value that is an average value of each pixel value of the first gray scale image data. In addition, the second filter 204 calculates, for each unit area, a unit area average value, which is an average value of pixel values of pixels belonging to the unit area, among the first gray scale image data. The size of the unit area used to remove the false color noise may be set differently from the unit area used to remove the impulsive noise or the Poisson noise, or the unit area used for the motion detection. Can be.

The second filter 204 may include a gray scale among pixels corresponding to the first gray scale image data and the second gray scale image data, that is, pixels whose coordinates are the same when the unit area average value exceeds an overall average value. Select pixels with large values.

When the unit area average value is equal to or less than the overall average value, the second filter 204 may include a gray scale value among pixels corresponding to the first gray scale image data and the second gray scale image data, that is, a pixel having the same coordinates. Select this small pixel.

The second filter 204 replaces the corresponding pixel of the first image data, that is, the pixel in the current frame with the selected pixel.

The above process may be expressed as Equation 7.

x ( k, j, t ) and y ( k, j, t ) represent gray scale values of pixels whose coordinates correspond to in the first image data and the second image data. Unit Area Average Represents the total mean value. The second filter 204 calculates the unit area average value and the overall average value for each frame, and selects a larger value or a smaller value from x ( k, j, t ) and x ( i, j, t- 1). This configuration is expressed as "dynamically calculating the threshold frame by frame."

In this manner, the second filter 204 checks the fall color noise (S308), and removes the fall color noise (S309). In addition, the second filter 204 calculates a threshold for each frame, thereby effectively detecting and removing false motion pixels that have not been detected by the prior art.

FIG. 7 is a diagram schematically illustrating a process of removing impulsive noise, Poisson noise, and false color noise from image data included in one or more segments divided into low brightness regions.

Referring to FIG. 7, impulse noise and Poisson noise are removed by the first filter, and false motion pixels mistaken as motion pixels by the motion detector are removed by the second filter using dynamic threshold. In addition, the first filter and the second filter according to the present embodiment are designed to remove noise while preserving detail and edge, respectively, as described above. As a result, when the image processing system 200 according to the present embodiment is used, Therefore, it is possible to output a clear image without noise from an image captured in an environment where there is not enough light.

8 is a view comparing the image processing result of the image processing method and system according to the present invention with the image processing result of the prior art. As described above, according to the present embodiment, the motion detection is performed after the first filter (functioning as a spatial filter) is applied to the image input from the camera and before the second filter (functioning as a temporal filter) is applied. Has a processing sequence.

Referring to FIG. 8, as a result of processing the image 801 input from the camera by the above processing sequence, the image 804 applies a spatial-temporal filter and performs motion detection according to the prior art. As a result of applying the motion detection and the spatio-temporal filter according to another conventional technique, the noise is removed more effectively than the image 803, and the image features are well highlighted in the low-light region. .

As described above, the present invention provides a method of first applying a first filter that performs a function of a spatial filter to an image, and first performing motion detection before applying a second filter that performs a function of a temporal filter to an image. By doing so, the smearing phenomenon on the surrounding pixels of the pixel where the false color noise is generated, which may occur as a result of the application of the second filter, is prevented from affecting the motion detection. Therefore, the motion detector can perform more accurate motion detection, and as a result, it is possible to output a quality image as a whole.

9 is a view comparing the relationship between the compression ratio of the image processing result image file and the PSNR of the image processing method and system according to an embodiment of the present invention with the image processing result according to the prior art. Since the image processing system according to the present exemplary embodiment outputs a clear image in which noise is removed and details are preserved, compared to the prior art, the compression efficiency is improved even when the image encoder 104 compresses the image.

9, an image file processed by an image processing method and system according to an embodiment of the present invention is more efficient than an image file 901 processed according to the prior art in terms of compression ratio and PSNR ratio 902. Im knowing.

Embodiments of the invention also include computer-readable media containing program instructions for performing various computer-implemented operations. The computer readable medium may include program instructions, data files, data structures, etc. alone or in combination. The medium or program instructions may be those specially designed and constructed for the purposes of the present invention, or they may be of the kind well-known and available to those having skill in the computer software arts. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tape, optical media such as CD-ROMs, DVDs, and magnetic disks, such as floppy disks. Magneto-optical media, and hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, flash memory, and the like. The medium may be a transmission medium such as an optical or metal wire, a waveguide, or the like including a carrier wave for transmitting a signal specifying a program command, a data structure, or the like. Examples of program instructions include not only machine code generated by a compiler, but also high-level language code that can be executed by a computer using an interpreter or the like.

10 is an internal block diagram of a general purpose computer system that may be employed to construct an image processing method and system in accordance with the present invention.

Computer system 1000 includes one or more processors 1001 connected to a main memory including random access memory (RAM) 1002 and read only memory (ROM) 1003. The processor 1001 is also called a central processing unit (CPU). As is well known in the art, the ROM 1003 serves to transfer data and instructions to the CPU unidirectionally, and the RAM 1002 typically transfers data and instructions bidirectionally. Used to. RAM 1002 and ROM 1003 may include any suitable form of computer readable media. Mass storage 1004 is bidirectionally coupled to processor 1001 to provide additional data storage capability and may be any of the computer readable recording media described above. The mass storage device 1004 is used to store programs, data, and the like, and is a secondary memory device such as a hard disk which is generally slower than the main memory device. Certain mass storage devices, such as CD ROM 1006, may also be used. The processor 1001 may include one or more input / output interfaces such as video monitors, trackballs, mice, keyboards, microphones, touchscreen displays, card readers, magnetic or paper tape readers, voice or handwriting readers, joysticks, or other known computer input / output devices. Connected to 1005. Finally, the processor 1001 may be connected to a wired or wireless communication network through the network interface 1007. Through this network connection, the procedure of the method described above can be performed. The apparatus and tools described above are well known to those skilled in the computer hardware and software arts.

The hardware device described above may be configured to operate as one or more software modules to perform the operations of the present invention.

According to the present invention, the image is divided into image data of a high brightness region and image data of a low brightness region to process only an image of a low brightness region in which noise is generated, thereby reducing the amount of computation and improving image processing efficiency. Methods and systems thereof are provided.

In addition, according to the present invention, by processing the image according to the spatial filtering, motion detecting, temporal filtering order, so that the smearing phenomenon to the surrounding pixels generated by the temporal filtering does not affect the motion detection more accurate motion Allow detection to be performed. Therefore, according to the present invention, it is possible to perform more efficient temporal filtering based on the accurate motion detection result, thereby effectively removing noise and providing a clear image as a result of the image processing.

In addition, according to the present invention, there is provided an image processing method and system capable of preserving edges or details while effectively removing impulsive noise or Poisson noise in the course of performing spatial filtering.

In addition, according to the present invention, by dynamically determining the threshold for each frame, an image processing method and system which can effectively remove false motion pixels mistaken as motion pixels as a result of motion detection and reduce smoothing phenomenon are provided. do.

As described above, the present invention has been described by way of limited embodiments and drawings, but the present invention is not limited to the above-described embodiments, which can be variously modified and modified by those skilled in the art to which the present invention pertains. Modifications are possible. Accordingly, the spirit of the present invention should be understood only by the claims set forth below, and all equivalent or equivalent modifications thereof will belong to the scope of the present invention.

1 is a diagram illustrating a case where an image processing system according to the present invention is used as a prefilter.

2 is a block diagram illustrating an image processing system according to an exemplary embodiment of the present invention.

3 is a flowchart illustrating an image processing method according to another exemplary embodiment of the present invention.

4 is a diagram exemplarily illustrating an entire image divided into a high brightness region and a low brightness region by a segmentation filter according to an embodiment of the present invention.

5 is a diagram for explaining impulsive noise and Poisson noise.

6 is a view for explaining a false motion pixel.

FIG. 7 schematically illustrates a process of removing impulsive noise, Poisson noise, and false color noise from image data included in one or more segments divided into low brightness regions according to an embodiment of the present invention. It is a figure shown.

8 is a view comparing the image processing result of the image processing method and system according to the present invention with the image processing result of the prior art.

9 is a view comparing the relationship between the compression ratio of the image processing result image file and the PSNR of the image processing method and system according to an embodiment of the present invention with the image processing result according to the prior art.

10 is an internal block diagram of a general purpose computer system that may be employed to construct an image processing method and system in accordance with the present invention.

<Explanation of symbols for the main parts of the drawings>

201: segmentation filter

202: first filter

203: motion detector

204: second filter

Claims (8)

In the image processing method for an image continuously input for each frame, Dividing the image into segments consisting of a predetermined number of pixels; A first step of dividing each segment into a low brightness region or a high brightness region by using brightness of image data included in each segment; A second step of removing impulsive noise from image data included in one or more segments divided into low brightness regions; Detecting a motion fixel among the image data; Detecting false color noise among the image data based on the detected motion pixel; And A fifth step of removing the detected false color noise from the image data Image processing method comprising a. The method of claim 1, And the second to fifth steps are not performed on the image data included in the segment divided into the high brightness region. The method of claim 1, The second step, Dividing the image data into predetermined unit areas; Detecting whether a first pixel including impulsive noise exists in the unit region by using a threshold; And Applying a median filter to the first pixel Image processing method comprising a. The method of claim 3, The threshold is, Determined by remind Is the minimum value for detecting pixels that contain impulsive noise, remind Is an average value of each pixel included in the unit region. In paragraph 4, Applying a mean-variance filter to a second pixel adjacent to the first pixel The image processing method further comprises. The method of claim 1, The fifth step, Determining first image data corresponding to a predetermined frame and image data corresponding to a frame input immediately after the frame as second image data; Converting the first image data to gray scale to generate first gray scale image data; Converting the second image data to gray scale to generate second gray scale image data; Calculating an overall mean value of the first gray scale image data; Calculating a unit area average value of the first gray scale image data for each unit area; Selecting a pixel having a large gray scale value from pixels corresponding to the first gray scale image data and the second gray scale image data when the unit area average value exceeds the total average value; Selecting a pixel having a small gray scale value among pixels corresponding to the first gray scale image data and the second gray scale image data when the unit area average value is less than or equal to the overall average value; Replacing a pixel of the first image data with the selected pixel Image processing method comprising a. A computer-readable recording medium having recorded thereon a program for implementing the method according to any one of claims 1 to 6 in a computer. In the image processing system for the image that is continuously input by frame, A segmentation filter dividing the image into segments composed of a predetermined number of pixels and dividing each segment into a low brightness region or a high brightness region by using brightness of image data included in each segment; A first filter for removing impulse noise from image data included in one or more segments divided into low brightness regions; A motion detector for detecting a motion fixel among the image data; And A second filter configured to detect false color noise among the image data based on the detected motion pixel and to remove the detected false color noise from the image data Image processing system comprising a.