CN111429351A - Image interpolation processing method and system - Google Patents
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Abstract
The invention discloses an image interpolation processing method and system, based on the method provided by the invention, when an image in a human body is obtained through an endoscope, 5 images are continuously collected and subjected to image contour extraction, then the image is divided into small blocks, the block with the maximum gray gradient value is determined in the small blocks, a new combined image is formed according to the determined blocks, then bilinear and cubic convolution interpolation calculation is carried out on the combined image, and finally an output image with high definition is output. By the method, the in-vivo image can be restored more accurately by precise image processing, and the error is reduced to the maximum extent. Thereby improving the restoration degree of the images in the human body collected by the endoscope system.
Description
Technical Field
The present application relates to the field of image processing technologies, and in particular, to an image interpolation processing method and system.
Background
With the improvement of medical level, the demand of medical devices in the medical community is more and more, and doctors must go through more efficient and deep medical examination and inspection devices to better determine the actual state of illness of patients and then make the most reasonable treatment.
The endoscope is designed for doctors to more accurately observe ulcers or tumors in the stomach of patients, and simultaneously transmits images in the stomach for the doctors to judge. The size of the endoscope and the accuracy of the image are very important because the endoscope is introduced into the body through the mouth of the patient.
Since the environment in the body is unstable, the algorithm of image processing is quite important.
However, the complexity of the current image processing method is high and the degree of restoration is also low.
Disclosure of Invention
The invention provides an image interpolation processing method and system, which are used for solving the problems of higher complexity and lower restoration degree of an image processing method in an endoscope in the prior art.
The specific technical scheme is as follows:
An image interpolation processing method, the method comprising:
When an image acquisition instruction is acquired, continuously acquiring N real-time images, wherein N is a positive integer greater than or equal to 2;
Reading the outline of each real-time image in the N real-time images, and dividing each image into K blocks;
Sequentially comparing K blocks corresponding to the N real-time images, and determining M blocks in the K blocks, wherein the M blocks are the blocks with the largest gray difference;
And combining the M blocks into a combined image, and performing bilinear interpolation calculation and cubic convolution interpolation calculation on the combined image to obtain an output image.
Optionally, reading an outline of each real-time image in the N real-time images, and dividing each image into K blocks, including:
Extracting the contours in 5 real-time images by an image contour extraction algorithm;
And (3) intercepting the gray gradient value of the contour in each real-time image from 5 real-time images, and dividing each image into 3-by-3 blocks.
Optionally, the K blocks corresponding to the N real-time images are sequentially compared, and the M blocks in the K blocks are determined, including:
Determining the block with the maximum gray gradient value in the obtained 3 x 3 blocks;
And screening the block with the maximum gray gradient value as the block for recombining the merged image.
An image interpolation processing system, the system comprising:
The acquisition module is used for continuously acquiring N real-time images when an image acquisition instruction is acquired, wherein N is a positive integer greater than or equal to 2;
The reading module is used for reading the outline of each real-time image in the N real-time images and dividing each image into K blocks;
The processing module is used for sequentially comparing the corresponding K blocks in the N real-time images and determining M blocks in the K blocks, wherein the M blocks are the blocks with the maximum gray difference; and combining the M blocks into a combined image, and performing bilinear interpolation calculation and cubic convolution interpolation calculation on the combined image to obtain an output image.
Optionally, the reading module is specifically configured to extract the contour of the 5 real-time images through an image contour extraction algorithm; and (3) intercepting the gray gradient value of the contour in each real-time image from 5 real-time images, and dividing each image into 3-by-3 blocks.
Optionally, the processing module is specifically configured to determine, from the obtained 3 × 3 blocks, a block with a maximum grayscale value; and screening the block with the maximum gray gradient value as the block for recombining the merged image.
Based on the method provided by the invention, when the image in the human body is obtained through the endoscope, 5 images are continuously collected and the image contour is extracted, then the image is divided into small blocks, the block with the maximum gray gradient value is determined in the small blocks, a new combined image is formed according to the determined blocks, then the combined image is subjected to bilinear and cubic convolution interpolation calculation, and finally an output image with high definition is output. By the method, the in-vivo image can be restored more accurately by precise image processing, and the error is reduced to the maximum extent. Thereby improving the restoration degree of the images in the human body collected by the endoscope system.
Drawings
FIG. 1 is a flowchart of an image interpolation processing method according to an embodiment of the present invention;
Fig. 2 is a schematic structural diagram of an image interpolation processing system according to an embodiment of the present invention.
Detailed Description
The technical solutions of the present invention are described in detail with reference to the drawings and the specific embodiments, and it should be understood that the embodiments and the specific technical features in the embodiments of the present invention are merely illustrative of the technical solutions of the present invention, and are not restrictive, and the embodiments and the specific technical features in the embodiments of the present invention may be combined with each other without conflict.
Fig. 1 is a flowchart of an image interpolation processing method according to an embodiment of the present invention, where the method includes:
S1, when an image acquisition instruction is obtained, continuously acquiring N real-time images;
N is a positive integer of 2 or more, and N is 5 in the embodiment of the present invention, but the value of N is not limited thereto. In practical applications, N may be a positive integer such as 6 or 7.
S2, reading the contour of each real-time image in the N real-time images, and dividing each image into K blocks;
K is a positive integer greater than or equal to 2, and K is equal to 3 × 3 in the embodiment of the present invention, but the value of K is not limited herein. In practical applications K may also be 4 x 4 or 5 x 5, etc.
S3, sequentially comparing the corresponding K blocks in the N real-time images, and determining M blocks in the K blocks;
Because the image is shot in a dynamic state, the algorithm can continuously shoot 5 pictures, firstly combines one picture through the analysis of 5 dynamic pictures, and then increases the resolution of the image through a plurality of interpolation modes.
The 5 dynamic images are combined to ensure the accuracy and definition of the pictures. Because the information points of the image generally exist in the outline of the image, the algorithm firstly extracts the outline of 5 images, intercepts the gray gradient value of the outline in each image, takes the part with the maximum gradient value and finally recombines the clearest image. That is, after dividing the real-time image into 3 × 3 blocks, 9 blocks of the image are determined to be re-stitched into a merged image.
And S4, combining the M blocks into a combined image, and performing bilinear interpolation calculation and cubic convolution interpolation calculation on the combined image to obtain an output image.
The technical solution of the present invention is explained in detail by the following specific examples.
First, the method is applied to a system, which can be an endoscope system, and the system comprises five parts:
1. A camera portion responsible for acquiring images;
2. A central processing part responsible for image processing;
3. Key part responsible for processing keys for man-machine interaction
4. A motor control part responsible for air exchange and supply;
5. And a display part responsible for displaying the result.
Based on the endoscope system, the method comprises the steps that after a user presses an image collecting button, the system continuously collects 5 real-time images, then the outlines of the 5 real-time images are read, each image is divided into 3 x 3 blocks, 9 small images corresponding to the 5 real-time images are sequentially compared, 9 small images with the maximum gray gradient difference are found out, the 9 small images are combined into a combined image, bilinear interpolation calculation and cubic convolution interpolation calculation are carried out on the combined image, and finally an output image is obtained.
And (3) image interpolation algorithm:
The system mainly increases the resolution of the image through an interpolation algorithm, so that the image is clearer.
Because the image is shot in a dynamic state, the algorithm can continuously shoot 5 pictures, firstly combines one picture through the analysis of 5 dynamic pictures, and then increases the resolution of the image through a plurality of interpolation modes.
1) Merging the dynamic pictures:
The 5 dynamic images are combined to ensure the accuracy and definition of the pictures. Because the information points of the image generally exist in the outline of the image, the algorithm firstly extracts the outline of 5 images, intercepts the gray gradient value of the outline in each image, takes the part with the maximum gradient value and finally recombines the clearest image.
In the algorithm, a Canny edge detection operator is adopted to identify the outline of the image.
The convolution operator used in the Canny operator is as follows:
The above formula is a template for calculating partial derivatives of the image in x and y directions, and the gradient amplitude of each point can be expressed by a mathematical formula as follows:
G[i，j]＝f[i，j]-f[i+1，j+1]|+|f[i+1，j]-f[i，j-1]|
The mathematical expressions of the first-order partial derivative matrix, the gradient amplitude and the gradient direction in the x direction and the y direction are as follows:
P[i，j]＝(t[i，j+1]-f[i，j]+f[i+1，j+1]-f[i+1，j])/2
Q[i，j]＝(f[i，j]-f[i+1，j]+f[i，j+1]-f[i+1，j+1])/2
θ[i，j]＝arctan(Q[i，j]/P[i，j])
The larger the value of an element in the image gradient magnitude matrix M [ i, j ], the larger the gradient value of the point in the image, i.e. the point is an edge, i.e. a contour portion of the image.
After the contours of the images are identified, the algorithm divides each image into 3 × 3 blocks, finds out the image with the maximum gradient value M [ i, j ] of the contour in each block respectively, and finally obtains 9 maximum gradient values respectively to form the primary image required by the user.
2) Bilinear interpolation algorithm:
For the obtained preliminary image, the system firstly carries out image interpolation through a bilinear interpolation algorithm, and then enlarges the image, and the system fully utilizes four real pixel values around a virtual point in the original image to jointly determine one pixel value in the target image.
For a target pixel, a floating-point coordinate obtained by inverse transformation of coordinates is set to be (i + u, j + v) (where i and j are both integer parts of the floating-point coordinate, and u and v are decimal parts of the floating-point coordinate and are floating-point numbers in an interval of taking a value of [0, 1), so that a value f (i + u, j + v) obtained by the pixel can be determined by values of four surrounding pixels corresponding to coordinates (i, j), (i +1, j), (i, j +1), (i +1, j +1) in an original image, that is: f (i + u, j + v) ═ 1-u (1-v) f (i, j) + (1-u) vf (i, j +1) + u (1-v) f (i +1, j) + uvf (i +1, j +1)
Where f (i, j) represents the pixel value at the source image (i, j), and so on.
When processing the image, firstly, according to: the method comprises the steps of calculating the position of a target pixel in a source image by using src (width)/dstwidth (src), and calculating the position of the target pixel in the source image by using src (width)/dst (height), wherein the calculated src and src are generally floating point numbers, for example, a pixel point f (1.2, 3.4) is virtual, and firstly finding four actually existing pixel points adjacent to her
(1，3) (2，3)
(1，4) (2，4)
Written in the form of f (i + u, j + v), then u is 0.2, v is 0.4, i is 1, j is 3;
Calculate f (i + u, j + v) ═ 1-u (1-v) f (i, j) + (1-u) vf (i, j +1) + u (1-v) f (i +1, j) + uvf (i +1, j + 1).
3) Cubic convolution interpolation algorithm:
After bilinear interpolation, the image is amplified to a certain extent, but if the bilinear amplification is too large, the distortion degree of the image is very large, so that after simple bilinear amplification, the image is amplified once again through a cubic convolution interpolation algorithm.
The cubic convolution interpolation algorithm considers 16 neighboring points around a floating-point coordinate (i + u, j + v), and the destination pixel value f (i + u, j + v) can be obtained by the following interpolation formula:
f(i+u，j+v)＝[A]*[B]*[C]
Wherein:
[A]＝[S(u+1) S(u+0) S(u-1) S(u-2)]
Based on the method provided by the invention, when the image in the human body is obtained through the endoscope, 5 images are continuously collected and the image contour is extracted, then the image is divided into small blocks, the block with the maximum gray gradient value is determined in the small blocks, a new combined image is formed according to the determined blocks, then the combined image is subjected to bilinear and cubic convolution interpolation calculation, and finally an output image with high definition is output. By the method, the in-vivo image can be restored more accurately by precise image processing, and the error is reduced to the maximum extent. Thereby improving the restoration degree of the images in the human body collected by the endoscope system.
Corresponding to the method provided by the present invention, an embodiment of the present invention further provides an image interpolation processing system, and as shown in fig. 2, the image interpolation processing system in the embodiment of the present invention is a schematic structural diagram, and the system includes:
The acquisition module 201 is configured to continuously acquire N real-time images when an image acquisition instruction is acquired, where N is a positive integer greater than or equal to 2;
The reading module 202 is used for reading the outline of each real-time image in the N real-time images and dividing each image into K blocks;
The processing module 203 is configured to sequentially compare K blocks corresponding to the N real-time images and determine M blocks of the K blocks, where the M blocks are blocks with the largest gray difference; and combining the M blocks into a combined image, and performing bilinear interpolation calculation and cubic convolution interpolation calculation on the combined image to obtain an output image.
Further, in the embodiment of the present invention, the reading module 202 is specifically configured to extract the contour of 5 real-time images through an image contour extraction algorithm; and (3) intercepting the gray gradient value of the contour in each real-time image from 5 real-time images, and dividing each image into 3-by-3 blocks.
Further, in this embodiment of the present invention, the processing module 203 is specifically configured to determine, from the obtained 3 × 3 blocks, a block with a maximum gray gradient value; and screening the block with the maximum gray gradient value as the block for recombining the merged image.
While the preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following appended claims be interpreted as including the preferred embodiment and all such alterations and modifications as fall within the scope of the application, including the use of specific symbols, labels, or other designations to identify the vertices.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.
Claims (6)
1. An image interpolation processing method, characterized by comprising:
When an image acquisition instruction is acquired, continuously acquiring N real-time images, wherein N is a positive integer greater than or equal to 2;
Reading the outline of each real-time image in the N real-time images, and dividing each image into K blocks;
Sequentially comparing K blocks corresponding to the N real-time images, and determining M blocks in the K blocks, wherein the M blocks are the blocks with the largest gray difference;
And combining the M blocks into a combined image, and performing bilinear interpolation calculation and cubic convolution interpolation calculation on the combined image to obtain an output image.
2. The method of claim 1, wherein reading the contour of each of the N live images and dividing each image into K blocks comprises:
Extracting the contours in 5 real-time images by an image contour extraction algorithm;
And (3) intercepting the gray gradient value of the contour in each real-time image from 5 real-time images, and dividing each image into 3-by-3 blocks.
3. The method of claim 1, wherein sequentially comparing K blocks corresponding to N real-time images and determining M of the K blocks comprises:
Determining the block with the maximum gray gradient value in the obtained 3 x 3 blocks;
And screening the block with the maximum gray gradient value as the block for recombining the merged image.
4. An image interpolation processing system, characterized in that the system comprises:
The acquisition module is used for continuously acquiring N real-time images when an image acquisition instruction is acquired, wherein N is a positive integer greater than or equal to 2;
The reading module is used for reading the outline of each real-time image in the N real-time images and dividing each image into K blocks;
The processing module is used for sequentially comparing the corresponding K blocks in the N real-time images and determining M blocks in the K blocks, wherein the M blocks are the blocks with the maximum gray difference; and combining the M blocks into a combined image, and performing bilinear interpolation calculation and cubic convolution interpolation calculation on the combined image to obtain an output image.
5. The system according to claim 4, wherein the reading module is specifically configured to extract contours from 5 real-time images by an image contour extraction algorithm; and (3) intercepting the gray gradient value of the contour in each real-time image from 5 real-time images, and dividing each image into 3-by-3 blocks.
6. The system according to claim 4, wherein the processing module is specifically configured to determine, among the 3 x 3 blocks obtained, the block with the largest gray gradient value; and screening the block with the maximum gray gradient value as the block for recombining the merged image.
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| CN101191719A (en) * | 2006-12-01 | 2008-06-04 | 鸿富锦精密工业（深圳）有限公司 | Image focal point synthetic system and method |
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