CN115345800A - Self-adaptive noise reduction method and system for medical endoscope moving image - Google Patents
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Abstract
The invention provides a self-adaptive noise reduction method and a self-adaptive noise reduction system for a medical endoscope moving image, and belongs to the technical field of image processing. Obtaining a first motion difference matrix according to the pixel difference value of the previous frame image and the current frame image, and obtaining a second motion difference matrix according to the pixel difference value of the current frame image and the next frame image; performing convolution filtering on the two motion difference value matrixes respectively, and adding corresponding pixels of the two motion difference value matrixes after the convolution filtering and calculating an average value to obtain a motion average value matrix; obtaining the transfer coefficient of each pixel of the motion mean matrix according to the motion mean matrix; obtaining a luminance component after noise reduction of each pixel of the mean matrix according to the transfer coefficient, the pixel luminance component of the previous frame image and the pixel luminance component of the current frame image, and obtaining a chrominance component after noise reduction of each pixel of the mean matrix according to the transfer coefficient, the pixel chrominance component of the previous frame image and the pixel chrominance component of the current frame image; the invention improves the noise reduction and filtering effects.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to a self-adaptive noise reduction method and a self-adaptive noise reduction system for a medical endoscope moving image.
Background
The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
The endoscope used in the medical field can enter a human body through a natural pore canal of the human body or a small incision made by operation, acquire and display an image of a certain part in the human body, and can assist a doctor in diagnosing the part.
The endoscope generally comprises a lens, a processor and a display device, wherein the lens can collect original image data and send the collected original image data to the processor, and the processor processes the obtained target image data and outputs the target image data to the display device for display. Various noises often exist in the use environment of the endoscope, such as the influence of thermal noise on a CMOS image sensor, the interference of high-frequency electric noise during video transmission and the like.
The inventor finds that the traditional spatial domain noise reduction method, including linear/nonlinear and spatial domain/frequency domain noise reduction algorithms, only processes the noise inside one frame of image, although the implementation is simpler, the edge of the image is easy to damage, and the noise reduction effect is general; the main idea of the traditional time domain noise reduction method is to utilize the correlation of multi-frame images in time to realize noise reduction, and the method is mainly suitable for processing static images and can generate artifacts or overlapping for moving objects in a picture.
Disclosure of Invention
In order to solve the defects of the prior art, the invention provides a self-adaptive noise reduction method and a self-adaptive noise reduction system for a medical endoscope moving image.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides a self-adaptive noise reduction method for a medical endoscope moving image.
A self-adaptive noise reduction method for a medical endoscope moving image comprises the following processes:
acquiring a current frame image and a previous frame image and a next frame image of the current frame image;
obtaining a first motion difference matrix according to the pixel difference value of the previous frame image and the current frame image, and obtaining a second motion difference matrix according to the pixel difference value of the current frame image and the next frame image;
convolution filtering is respectively carried out on the first motion difference matrix and the second motion difference matrix, corresponding pixels of the first motion difference matrix and the second motion difference matrix after convolution filtering are added and mean value is obtained, and then a motion mean value matrix is obtained;
obtaining the transfer coefficient of each pixel of the motion mean matrix according to the motion mean matrix;
and obtaining a luminance component after noise reduction of each pixel of the mean value matrix according to the transfer coefficient, the pixel luminance component of the previous frame image and the pixel luminance component of the current frame image, and obtaining a chrominance component after noise reduction of each pixel of the mean value matrix according to the transfer coefficient, the pixel chrominance component of the previous frame image and the pixel chrominance component of the current frame image.
As an optional implementation manner, the first motion difference matrix and the second motion difference matrix are respectively subjected to two-dimensional convolution space filtering by using a normalized mean convolution kernel.
As an optional implementation manner, the luminance component after noise reduction of any pixel is: the product of the difference value and the transfer coefficient of the brightness component corresponding to the previous frame image and the current frame image of the pixel, and the sum of the brightness components corresponding to the current frame image of the pixel;
or, the luminance component after noise reduction of any pixel is: the difference value between 1 and the transfer coefficient is multiplied by the brightness component corresponding to the current frame image of the pixel, and then is added with the product of the transfer coefficient and the brightness component corresponding to the previous frame image of the pixel.
As an optional implementation manner, the chrominance component after noise reduction of any pixel is: the product of the difference value and the transfer coefficient of the chroma component corresponding to the previous frame image and the chroma component corresponding to the current frame image of the pixel, and the sum of the chroma components corresponding to the current frame image of the pixel;
or, the chrominance component after noise reduction of any pixel is: the difference value between 1 and the transfer coefficient is multiplied by the chrominance component corresponding to the current frame image of the pixel, and then is added with the product of the transfer coefficient and the chrominance component corresponding to the previous frame image of the pixel.
As an optional implementation manner, obtaining a transfer coefficient of each pixel of the motion mean matrix according to the motion mean matrix includes:
calculating pixel difference values of a previous frame image and a current frame image to obtain a difference matrix, dividing the difference matrix into a plurality of partitions, respectively calculating element mean values in each partition of the difference matrix, and selecting the maximum mean value of each partition as a calibration value;
selecting a corresponding transfer function lookup table according to the calibration value;
and taking the pixels in the mean value matrix as indexes, and obtaining the transfer coefficient corresponding to each pixel by inquiring the transfer function lookup table.
Further, the generation of the transfer coefficients in the transfer function lookup table includes:
and obtaining a motion transfer coefficient corresponding to each pixel of the motion mean matrix according to the noise reduction coefficient, the pixel value of the motion mean matrix and the exponential function model.
As an optional implementation manner, obtaining a transfer coefficient of each pixel of the motion mean matrix according to the motion mean matrix includes:
and directly obtaining the motion transfer coefficient corresponding to each pixel of the motion mean value matrix according to the noise reduction coefficient, the pixel value of the motion mean value matrix and the exponential function model.
The invention provides a medical endoscope moving image self-adaptive noise reduction system in a second aspect.
The utility model provides a medical endoscope motion image self-adaptation noise reduction system which characterized in that:
the method comprises the following steps:
a data acquisition module configured to: acquiring a current frame image and a previous frame image and a next frame image of the current frame image;
a difference matrix acquisition module configured to: obtaining a first motion difference matrix according to the pixel difference value of the previous frame image and the current frame image, and obtaining a second motion difference matrix according to the pixel difference value of the current frame image and the next frame image;
a mean matrix acquisition module configured to: convolution filtering is respectively carried out on the first motion difference matrix and the second motion difference matrix, corresponding pixels of the first motion difference matrix and the second motion difference matrix after convolution filtering are added and mean value is obtained, and then a motion mean value matrix is obtained;
a transfer coefficient acquisition module configured to: obtaining the transfer coefficient of each pixel of the motion mean matrix according to the motion mean matrix;
a noise reduction component generation module configured to: and obtaining a luminance component after noise reduction of each pixel of the mean value matrix according to the transfer coefficient, the pixel luminance component of the previous frame image and the pixel luminance component of the current frame image, and obtaining a chrominance component after noise reduction of each pixel of the mean value matrix according to the transfer coefficient, the pixel chrominance component of the previous frame image and the pixel chrominance component of the current frame image.
As an optional implementation manner, in the mean matrix obtaining module, the normalized mean convolution kernel is respectively used for the first motion difference matrix and the second motion difference matrix to perform two-dimensional convolution spatial filtering.
As an optional implementation manner, in the noise reduction component generation module, the luminance component after any pixel is subjected to noise reduction is: the product of the difference value and the transfer coefficient of the brightness component corresponding to the previous frame image and the current frame image of the pixel, and the sum of the brightness components corresponding to the current frame image of the pixel;
or in the noise reduction component generation module, the luminance component after any pixel is subjected to noise reduction is as follows: the product of the difference value of 1 and the transfer coefficient and the brightness component corresponding to the current frame image of the pixel, and the sum of the product of the transfer coefficient and the brightness component corresponding to the previous frame image of the pixel.
As an optional implementation manner, in the noise reduction component generation module, the chrominance component after noise reduction of any pixel is: the product of the difference value and the transfer coefficient of the chroma component corresponding to the previous frame image and the chroma component corresponding to the current frame image of the pixel, and the sum of the chroma components corresponding to the current frame image of the pixel;
or, in the noise reduction component generation module, the chrominance component after any pixel is subjected to noise reduction is as follows: the difference value between 1 and the transfer coefficient is multiplied by the chrominance component corresponding to the current frame image of the pixel, and then is added with the product of the transfer coefficient and the chrominance component corresponding to the previous frame image of the pixel.
As an optional implementation manner, obtaining a transfer coefficient of each pixel of the motion mean matrix according to the motion mean matrix includes:
dividing the motion mean matrix into a plurality of partitions, calculating the mean value of pixel difference values of a previous frame image and a current frame image corresponding to each partition, and taking the maximum mean value of each partition as a calibration value;
selecting a corresponding transfer function lookup table according to the calibration value;
and taking the pixels in the mean value matrix as indexes, and obtaining the transfer coefficient corresponding to each pixel by inquiring the transfer function lookup table.
Further, the generation of the transfer coefficients in the transfer function lookup table includes:
and obtaining a motion transfer coefficient corresponding to each pixel of the motion mean value matrix according to the noise reduction coefficient, the pixel value of the motion mean value matrix and the exponential function model.
As an optional implementation manner, obtaining a transfer coefficient of each pixel of the motion mean matrix according to the motion mean matrix includes:
and directly obtaining the motion transfer coefficient corresponding to each pixel of the motion mean value matrix according to the noise reduction coefficient, the pixel value of the motion mean value matrix and the exponential function model.
A third aspect of the present invention provides a computer-readable storage medium, on which a program is stored, which when executed by a processor, performs the steps in the medical endoscope moving image adaptive noise reduction method according to the first aspect of the present invention.
A fourth aspect of the present invention provides an electronic device, comprising a memory, a processor and a program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the adaptive noise reduction method for moving images of a medical endoscope according to the first aspect of the present invention.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the medical endoscope moving image self-adaptive noise reduction method and system, the noise reduction processing is carried out on the current video frame by calculating the motion quantity value between the adjacent video frames, and the noise reduction and filtering effects are improved.
2. According to the self-adaptive noise reduction method and system for the moving image of the medical endoscope, the noise-reduced brightness component of each pixel of the mean value matrix is obtained according to the transfer coefficient, the pixel brightness component of the previous frame image and the pixel brightness component of the current frame image, and effective noise reduction of the brightness component is achieved.
3. According to the self-adaptive noise reduction method and system for the moving image of the medical endoscope, the chroma component of each pixel of the mean value matrix after noise reduction is obtained according to the transfer coefficient, the pixel chroma component of the previous frame image and the pixel chroma component of the current frame image, and effective noise reduction of the chroma component is achieved.
4. According to the self-adaptive noise reduction method and system for the medical endoscope moving image, the motion transfer coefficient corresponding to each pixel of the moving mean matrix is obtained according to the noise reduction coefficient, the pixel value of the moving mean matrix and the exponential function model, and the problems of smearing and ghost image caused by a recursive filter can be effectively solved.
Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
Fig. 1 is a schematic view of a single-frame image denoising process flow provided in embodiment 1 of the present invention.
Fig. 2 is a schematic flowchart of the luminance component noise reduction processing provided in embodiment 1 of the present invention.
Fig. 3 is a schematic flow chart of the Cr chrominance component noise reduction process provided in embodiment 1 of the present invention.
Fig. 4 is a schematic flowchart of the Cb chrominance component noise reduction process according to embodiment 1 of the present invention.
Fig. 5 is a schematic diagram of a motion transfer function curve provided in embodiment 1 of the present invention.
Fig. 6 is a diagram of an image effect before adaptive noise reduction according to embodiment 1 of the present invention.
Fig. 7 is a diagram of an image effect after adaptive noise reduction according to embodiment 1 of the present invention.
Fig. 8 is a schematic frame diagram of a medical endoscope moving image adaptive noise reduction system according to embodiment 2 of the present invention.
Detailed Description
The invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The embodiments and features of the embodiments of the present invention may be combined with each other without conflict.
Example 1:
the embodiment 1 of the invention provides a self-adaptive noise reduction method for a medical endoscope moving image, which comprises the following processes:
acquiring a current frame image and a previous frame image and a next frame image of the current frame image;
obtaining a first motion difference matrix according to the pixel difference value of the previous frame image and the current frame image, and obtaining a second motion difference matrix according to the pixel difference value of the current frame image and the next frame image;
convolution filtering is respectively carried out on the first motion difference matrix and the second motion difference matrix, corresponding pixels of the first motion difference matrix and the second motion difference matrix after convolution filtering are added and mean value is obtained, and then a motion mean value matrix is obtained;
obtaining the transfer coefficient of each pixel of the motion mean matrix according to the motion mean matrix;
and obtaining a brightness component after noise reduction of each pixel of the mean matrix according to the transfer coefficient, the pixel brightness component of the previous frame image and the pixel brightness component of the current frame image, and obtaining a chrominance component after noise reduction of each pixel of the mean matrix according to the transfer coefficient, the pixel chrominance component of the previous frame image and the pixel chrominance component of the current frame image.
Specifically, a preferred implementation manner includes the following processes:
s1: acquiring a current frame image and a previous frame image and a next frame image of the current frame image, and calculating the difference value between the previous frame image and the next frame image to obtain two motion difference value matrixes;
s2: performing two-dimensional convolution space filtering on the two motion difference matrixes, adopting a 5 multiplied by 5 normalized mean convolution kernel to eliminate noise interference, adding the two matrixes after convolution operation, and calculating the mean value of the two matrixes to obtain a motion mean matrix, which is called a mean matrix for short;
s3: dividing the image into 16 partitions, calculating the mean value of the motion difference values of the previous frame and the current frame of each partition, and selecting the maximum mean value of each partition as a calibration value as a basis for selecting a motion transfer function;
s4: selecting a corresponding transfer function lookup table according to the calibration value;
s5: taking elements in the mean matrix as indexes, obtaining a transfer coefficient M by inquiring a transfer function lookup table, and obtaining a "transfer coefficient M matrix" with the same row number as the "mean matrix", which is hereinafter referred to as "M matrix";
s6: for a YCrCb 4. The three components are processed in the same way, and a calculation formula is given below by taking the Y component as an example;
yout = M (Ypre-Yin) + Yin = M (Ypre + Yin + (1-M) × Yin (formula 1)
Wherein: m is a transmission coefficient obtained by looking up a table; ypre is the brightness component of the corresponding pixel of the previous frame image; yin is the corresponding pixel brightness component of the current frame image; yout is the corresponding pixel brightness component of the image after noise reduction.
For the Cr component and the Cb component, the calculation method is the same as the above formula, and only the corresponding luminance component needs to be modified into the chrominance component, which is not described herein again.
As shown in fig. 1, which is a schematic diagram of a processing flow of single frame image denoising, a video processor continuously receives three frames of YCrCb images, which are respectively numbered Fpre, fin, and Fbeh in sequence, extracts a luminance component Ypre of a previous frame image, a luminance component Yin of a current frame image, and a luminance component Ybeh of a next frame image, and performs processing as shown in fig. 2, where ABS represents calculating an absolute value after convolution processing of two motion difference matrices, and calculates an image mean matrix; dividing a partition of the mean value matrix, determining a current image calibration value, selecting a motion transfer function according to the calibration value, and calculating according to a formula 1 to obtain a noise-reduced brightness component Yout; calculating the chromaticity components of the three images based on the same calculation mode; similarly, extracting a Cr chrominance component Cpre1 of a previous frame image, a Cr chrominance component Cin1 of a current frame image and a Cr chrominance component Cbeh1 of a next frame image, and processing according to the flow of the figure 3 to obtain a Cr chrominance component Cout1 after noise reduction; and extracting a Cb chrominance component Cpre2 of the previous frame image Cb, a Cb chrominance component Cin2 of the current frame image Cb and a Cb chrominance component Cbeh2 of the next frame image Cb, and processing according to the flow of the figure 4 to obtain a Cb chrominance component Cout2 after noise reduction.
In this embodiment, the transfer coefficient in the selected transfer function lookup table is calculated according to a specific Motion Transfer Function (MTF), the present embodiment refers to a probability density function of noise superimposed on a video stream, and an exponential function is very effective for eliminating smearing and ghost problems caused by a recursive filter, and the present embodiment adopts the following exponential function curve:
Wherein: y is a motion transfer coefficient M; x is the corresponding mean matrix element; and n is a noise reduction coefficient.
It can be understood that, in some other embodiments, a table lookup manner may not be adopted here, and the transfer coefficient corresponding to each pixel is obtained by directly performing calculation according to each pixel of the mean value matrix in combination with the formula 2, and those skilled in the art may select the transfer coefficient according to specific working conditions, which is not described here again.
As shown in fig. 5, the abscissa corresponds to x in formula 2, and the ordinate represents y in formula 2, i.e., the motion transfer coefficient, as shown in fig. 5, the value range of x is 0 to 63, the values of the elements of the mean matrix exceeding the range are all converted into a boundary threshold 63, the y component is the motion transfer coefficient, the value is amplified by 255 times, and the range is 0 to 255; fig. 5 contains the function curves of four noise reduction coefficients, with the noise reduction coefficients n from high to low being 10, 8, 4, 1, respectively.
In this embodiment, the larger the motion difference between the previous and next frame images and the current image is, the smaller the corresponding motion transfer function search result is; the smaller the image motion difference value is, the larger the motion transfer function search result is, the MTF table item selection is matched with the video flow direction, and the influence caused by smearing artifacts is reduced.
As shown in fig. 6 and 7, for comparing the images before and after the adaptive noise reduction function, it can be seen that the method described in this embodiment can effectively implement image noise reduction.
Example 2:
as shown in fig. 8, embodiment 2 of the present invention provides a medical endoscope moving image adaptive noise reduction system, which is characterized in that:
the method comprises the following steps:
a data acquisition module configured to: acquiring a current frame image and a previous frame image and a next frame image of the current frame image;
a difference matrix acquisition module configured to: obtaining a first motion difference matrix according to the pixel difference value of the previous frame image and the current frame image, and obtaining a second motion difference matrix according to the pixel difference value of the current frame image and the next frame image;
a mean matrix acquisition module configured to: convolution filtering is respectively carried out on the first motion difference matrix and the second motion difference matrix, corresponding pixels of the first motion difference matrix and the second motion difference matrix after convolution filtering are added and mean value is obtained, and then a motion mean value matrix is obtained;
a transfer coefficient acquisition module configured to: obtaining the transfer coefficient of each pixel of the motion mean matrix according to the motion mean matrix;
a noise reduction component generation module configured to: and obtaining a luminance component after noise reduction of each pixel of the mean value matrix according to the transfer coefficient, the pixel luminance component of the previous frame image and the pixel luminance component of the current frame image, and obtaining a chrominance component after noise reduction of each pixel of the mean value matrix according to the transfer coefficient, the pixel chrominance component of the previous frame image and the pixel chrominance component of the current frame image.
Specifically, a preferred implementation includes the following processes:
the difference matrix obtaining module comprises: acquiring a current frame image and a previous frame image and a next frame image of the current frame image, and calculating the difference value between the previous frame image and the next frame image to obtain two motion difference value matrixes.
The mean matrix acquisition module comprises: two-dimensional convolution space filtering is carried out on the two motion difference value matrixes, 5 multiplied by 5 normalized mean convolution kernels are adopted to eliminate noise interference, the two matrixes after convolution operation are added, and the mean value of the two matrixes is calculated to obtain a motion mean value matrix, which is called a mean value matrix for short.
The transmission coefficient obtaining module comprises:
dividing the image into 16 partitions, calculating the mean value of the motion difference values of the previous frame and the current frame of each partition, and selecting the maximum mean value of each partition as a calibration value as a basis for selecting a motion transfer function;
selecting a corresponding transfer function lookup table according to the calibration value;
taking the elements in the mean matrix as indexes, obtaining a transfer coefficient M by inquiring a transfer function lookup table, and obtaining a "transfer coefficient M matrix" with the same row number as the "mean matrix", which is hereinafter referred to as "M matrix".
The noise reduction component generation module comprises:
for a YCrCb 4. The three components are processed in the same way, and a calculation formula is given below by taking the Y component as an example;
yout = M (Ypre-Yin) + Yin = M (Ypre + Yin + (1-M) × Yin (formula 1)
Wherein: m is a transfer coefficient obtained by table look-up; ypre is the brightness component of the corresponding pixel of the previous frame image; yin is the pixel brightness component corresponding to the current frame image; yout is the corresponding pixel brightness component of the image after noise reduction.
For the Cr component and the Cb component, the calculation method is the same as the above formula, and only the corresponding luminance component needs to be modified into the chrominance component, which is not described herein again.
As shown in fig. 1 of embodiment 1, which is a schematic diagram of a processing flow of single-frame image noise reduction, a video processor continuously receives three frames of YCrCb images, which are respectively numbered Fpre, fin, and Fbeh in sequence, extracts a luminance component Ypre of a previous frame image, a luminance component Yin of a current frame image, and a luminance component Ybeh of a next frame image, and performs processing as shown in fig. 2 of embodiment 1, where ABS represents calculating an absolute value after convolution processing of two motion difference matrices, and calculates an image mean matrix; dividing a partition of the mean value matrix, determining a current image calibration value, selecting a motion transfer function according to the calibration value, and calculating according to a formula 1 to obtain a noise-reduced brightness component Yout; calculating the chromaticity components of the three images based on the same calculation mode; similarly, extracting a Cr chrominance component Cpre1 of the previous frame image, a Cr chrominance component Cin1 of the current frame image, and a Cr chrominance component Cbeh1 of the next frame image, and processing according to the flow of fig. 3 in embodiment 1 to obtain a Cr chrominance component Cout1 after noise reduction; extracting the Cb chrominance component Cpre2 of the previous frame image, the Cb chrominance component Cin2 of the current frame image, and the Cb chrominance component Cbeh2 of the next frame image, and processing according to the flow of fig. 4 in embodiment 1 to obtain the Cb chrominance component Cout2 after noise reduction.
In this embodiment, the transfer coefficient in the selected transfer function lookup table is calculated according to a specific Motion Transfer Function (MTF), the present embodiment refers to a probability density function of noise superimposed on a video stream, and an exponential function is very effective for eliminating smearing and ghost problems caused by a recursive filter, and the present embodiment adopts the following exponential function curve:
Wherein: y is a motion transfer coefficient M; x is the corresponding mean matrix element; and n is a noise reduction coefficient.
It may be understood that, in some other embodiments, a table lookup manner may not be adopted, and the transfer coefficient corresponding to each pixel is obtained by directly performing calculation according to each pixel of the mean value matrix and combining the formula 2, which may be selected by a person skilled in the art according to a specific working condition and is not described herein again.
As shown in fig. 5 in embodiment 1, the function curves corresponding to different noise reduction adjustment coefficients are obtained, where the abscissa corresponds to x in formula 2, the ordinate represents y in formula 2, i.e., the motion transfer coefficient, the value range of x is 0 to 63, and the values of the elements of the mean matrix exceeding the range are all converted into the boundary threshold 63; the y component is a motion transfer coefficient, the value is enlarged by 255 times, and the range is 0 to 255; fig. 5 contains curves of the function of four noise reduction coefficients, n being 10, 8, 4, 1 from high to low.
In this embodiment, the larger the motion difference between the previous and next frame images and the current image is, the smaller the corresponding motion transfer function search result is; the smaller the image motion difference value is, the larger the motion transfer function search result is, the MTF table item selection is matched with the video flow direction, and the influence caused by smearing artifacts is reduced.
As shown in fig. 6 and 7 in embodiment 1, for comparing the effects before and after the adaptive noise reduction function with the images, it can be seen that the method described in this embodiment can effectively implement image noise reduction.
Example 3:
embodiment 3 of the present invention provides a computer-readable storage medium on which a program is stored, which, when executed by a processor, implements the steps in the medical endoscope moving image adaptive noise reduction method according to embodiment 1 of the present invention.
Example 4:
embodiment 4 of the present invention provides an electronic device, which includes a memory, a processor, and a program stored in the memory and executable on the processor, where the processor implements the steps in the medical endoscope moving image adaptive noise reduction method according to embodiment 1 of the present invention when executing the program.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present invention has been described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (16)
1. A self-adaptive noise reduction method for a medical endoscope moving image is characterized by comprising the following steps:
the method comprises the following steps:
acquiring a current frame image and a previous frame image and a next frame image of the current frame image;
obtaining a first motion difference matrix according to the pixel difference value of the previous frame image and the current frame image, and obtaining a second motion difference matrix according to the pixel difference value of the current frame image and the next frame image;
convolution filtering is respectively carried out on the first motion difference matrix and the second motion difference matrix, corresponding pixels of the first motion difference matrix and the second motion difference matrix after convolution filtering are added and mean value is obtained, and then a motion mean value matrix is obtained;
obtaining the transfer coefficient of each pixel of the motion mean matrix according to the motion mean matrix;
and obtaining a luminance component after noise reduction of each pixel of the mean value matrix according to the transfer coefficient, the pixel luminance component of the previous frame image and the pixel luminance component of the current frame image, and obtaining a chrominance component after noise reduction of each pixel of the mean value matrix according to the transfer coefficient, the pixel chrominance component of the previous frame image and the pixel chrominance component of the current frame image.
2. The adaptive noise reduction method for moving images of medical endoscopes according to claim 1, characterized in that:
and respectively adopting a normalized mean convolution kernel to carry out two-dimensional convolution space filtering on the first motion difference matrix and the second motion difference matrix.
3. The adaptive noise reduction method for moving images of medical endoscopes according to claim 1, characterized in that:
the luminance component after any pixel is denoised is: the product of the difference value and the transfer coefficient of the brightness component corresponding to the previous frame image and the current frame image of the pixel, and the sum of the brightness components corresponding to the current frame image of the pixel;
or, the luminance component after noise reduction of any pixel is: the product of the difference value of 1 and the transfer coefficient and the brightness component corresponding to the current frame image of the pixel, and the sum of the product of the transfer coefficient and the brightness component corresponding to the previous frame image of the pixel.
4. The adaptive noise reduction method for moving images of medical endoscopes according to claim 1, characterized in that:
the chrominance components after noise reduction of any pixel are: the product of the difference value and the transfer coefficient of the chroma component corresponding to the previous frame image and the chroma component corresponding to the current frame image of the pixel, and the sum of the chroma components corresponding to the current frame image of the pixel;
or, the chrominance component after noise reduction of any pixel is: the difference value between 1 and the transfer coefficient is multiplied by the chrominance component corresponding to the current frame image of the pixel, and then is added with the product of the transfer coefficient and the chrominance component corresponding to the previous frame image of the pixel.
5. The adaptive noise reduction method for moving images of medical endoscopes according to claim 1, characterized in that:
obtaining a transfer coefficient of each pixel of the motion mean matrix according to the motion mean matrix, comprising:
calculating pixel difference values of a previous frame image and a current frame image to obtain a difference matrix, dividing the difference matrix into a plurality of partitions, respectively calculating element mean values in each partition of the difference matrix, and selecting the maximum mean value of each partition as a calibration value;
selecting a corresponding transfer function lookup table according to the calibration value;
and taking the pixels in the mean value matrix as indexes, and obtaining the transfer coefficient corresponding to each pixel by inquiring the transfer function lookup table.
6. The adaptive noise reduction method for moving images of medical endoscopes according to claim 5, characterized in that:
the generation of transfer coefficients in a transfer function look-up table, comprising:
and obtaining a motion transfer coefficient corresponding to each pixel of the motion mean value matrix according to the noise reduction coefficient, the pixel value of the motion mean value matrix and the exponential function model.
7. The adaptive noise reduction method for moving images of medical endoscopes according to claim 1, characterized in that:
obtaining a transfer coefficient of each pixel of the motion mean matrix according to the motion mean matrix, comprising:
and directly obtaining the motion transfer coefficient corresponding to each pixel of the motion mean value matrix according to the noise reduction coefficient, the pixel value of the motion mean value matrix and the exponential function model.
8. The utility model provides a medical endoscope motion image self-adaptation noise reduction system which characterized in that:
the method comprises the following steps:
a data acquisition module configured to: acquiring a current frame image and a previous frame image and a next frame image of the current frame image;
a difference matrix acquisition module configured to: obtaining a first motion difference matrix according to the pixel difference value of the previous frame image and the current frame image, and obtaining a second motion difference matrix according to the pixel difference value of the current frame image and the next frame image;
a mean matrix acquisition module configured to: convolution filtering is respectively carried out on the first motion difference matrix and the second motion difference matrix, corresponding pixels of the first motion difference matrix and the second motion difference matrix after convolution filtering are added and mean value is obtained, and then a motion mean value matrix is obtained;
a transfer coefficient acquisition module configured to: obtaining the transfer coefficient of each pixel of the motion mean matrix according to the motion mean matrix;
a noise reduction component generation module configured to: and obtaining a luminance component after noise reduction of each pixel of the mean value matrix according to the transfer coefficient, the pixel luminance component of the previous frame image and the pixel luminance component of the current frame image, and obtaining a chrominance component after noise reduction of each pixel of the mean value matrix according to the transfer coefficient, the pixel chrominance component of the previous frame image and the pixel chrominance component of the current frame image.
9. The adaptive noise reduction system for moving images of medical endoscopes according to claim 8, wherein:
and in the mean value matrix acquisition module, a normalized mean value convolution kernel is respectively adopted for the first motion difference value matrix and the second motion difference value matrix to carry out two-dimensional convolution space filtering.
10. The medical endoscope motion image adaptive noise reduction system of claim 8, characterized by:
in the noise reduction component generation module, the luminance component after any pixel is subjected to noise reduction is as follows: the product of the difference value and the transfer coefficient of the brightness component corresponding to the previous frame image and the current frame image of the pixel, and the sum of the brightness components corresponding to the current frame image of the pixel;
or in the noise reduction component generation module, the luminance component after any pixel is subjected to noise reduction is as follows: the difference value between 1 and the transfer coefficient is multiplied by the brightness component corresponding to the current frame image of the pixel, and then is added with the product of the transfer coefficient and the brightness component corresponding to the previous frame image of the pixel.
11. The adaptive noise reduction system for moving images of medical endoscopes according to claim 8, wherein:
in the noise reduction component generation module, the chrominance component after any pixel is subjected to noise reduction is as follows: the product of the difference value and the transfer coefficient of the chrominance component corresponding to the previous frame image and the chrominance component corresponding to the current frame image of the pixel, and the addition of the chrominance components corresponding to the current frame image of the pixel;
or, in the noise reduction component generation module, the chrominance component after any pixel is subjected to noise reduction is as follows: the difference value between 1 and the transfer coefficient is multiplied by the chrominance component corresponding to the current frame image of the pixel, and then is added with the product of the transfer coefficient and the chrominance component corresponding to the previous frame image of the pixel.
12. The adaptive noise reduction system for moving images of medical endoscopes according to claim 8, wherein:
obtaining a transfer coefficient of each pixel of the motion mean value matrix according to the motion mean value matrix, wherein the transfer coefficient comprises the following steps:
dividing the motion mean matrix into a plurality of partitions, calculating the mean value of pixel difference values of a previous frame image and a current frame image corresponding to each partition, and taking the maximum mean value of each partition as a calibration value;
selecting a corresponding transfer function lookup table according to the calibration value;
and taking the pixels in the mean value matrix as indexes, and obtaining the transfer coefficient corresponding to each pixel by inquiring the transfer function lookup table.
13. The adaptive noise reduction system for moving images of medical endoscopes according to claim 12, wherein:
the generation of transfer coefficients in a transfer function look-up table, comprising:
and obtaining a motion transfer coefficient corresponding to each pixel of the motion mean value matrix according to the noise reduction coefficient, the pixel value of the motion mean value matrix and the exponential function model.
14. The adaptive noise reduction system for moving images of medical endoscopes according to claim 8, wherein:
obtaining a transfer coefficient of each pixel of the motion mean matrix according to the motion mean matrix, comprising:
and directly obtaining the motion transfer coefficient corresponding to each pixel of the motion mean value matrix according to the noise reduction coefficient, the pixel value of the motion mean value matrix and the exponential function model.
15. A computer-readable storage medium, on which a program is stored, which, when being executed by a processor, carries out the steps of the method for adaptive noise reduction of moving images of a medical endoscope according to any one of claims 1 to 7.
16. An electronic device comprising a memory, a processor and a program stored in the memory and executable on the processor, wherein the processor implements the steps of the adaptive noise reduction method for moving images of a medical endoscope according to any one of claims 1-7 when executing the program.
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