CN114418920A - Endoscope multi-focus image fusion method - Google Patents

Endoscope multi-focus image fusion method Download PDF

Info

Publication number
CN114418920A
CN114418920A CN202210320964.9A CN202210320964A CN114418920A CN 114418920 A CN114418920 A CN 114418920A CN 202210320964 A CN202210320964 A CN 202210320964A CN 114418920 A CN114418920 A CN 114418920A
Authority
CN
China
Prior art keywords
image
images
corrected
color
light
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202210320964.9A
Other languages
Chinese (zh)
Other versions
CN114418920B (en
Inventor
于腾波
马金龙
王辰
陈进利
付海涛
王晓南
王坤
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Affiliated Hospital of University of Qingdao
Original Assignee
Affiliated Hospital of University of Qingdao
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Affiliated Hospital of University of Qingdao filed Critical Affiliated Hospital of University of Qingdao
Priority to CN202210320964.9A priority Critical patent/CN114418920B/en
Publication of CN114418920A publication Critical patent/CN114418920A/en
Application granted granted Critical
Publication of CN114418920B publication Critical patent/CN114418920B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/50—Image enhancement or restoration by the use of more than one image, e.g. averaging, subtraction
    • G06T5/90—
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/90—Determination of colour characteristics
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10068—Endoscopic image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20212—Image combination
    • G06T2207/20221—Image fusion; Image merging

Abstract

The invention discloses an endoscope multi-focus image fusion method, which belongs to the technical field of image data processing and comprises the following steps: an image acquisition step, wherein images are acquired under different focal lengths respectively to obtain a plurality of original images; a characteristic point extraction step, which is to respectively extract the characteristic points of each original image; a characteristic matching step, namely obtaining matching point pairs between every two original images; determining a reference image, and searching the reference image; correcting all original images to the plane of the reference image to obtain a plurality of corrected images; a step of light and color homogenizing treatment, namely obtaining a corrected image after light and color homogenizing; and an image fusion step, namely performing fusion processing on the corrected images after light and color uniformization to obtain a fused image. According to the endoscope multi-focus image fusion method, the collected images with different focus distances are fused to obtain an image containing clear positions of all the collected images, and a user can observe the image conveniently.

Description

Endoscope multi-focus image fusion method
Technical Field
The invention belongs to the technical field of image processing, and particularly relates to an endoscope multi-focus image fusion method.
Background
The endoscope is used as a detection instrument based on an image sensor, can see lesions which cannot be displayed by X-rays, and is widely applied to the field of medical treatment. Most of the existing endoscope photosensitive devices are CCD or CMOS, the imaging principle is pinhole imaging, and the imaging clear range is limited by the depth of field.
In addition, when an endoscope is used for imaging in some organs of a human body, a specific pathological area needs to be found, but because the intestinal tract in the body of the human body is always in a peristalsis state, the resolution of the shot pathological area is extremely low, and even along with the peristalsis of the intestinal tract, the pathological area cannot be kept in the middle of the image, so that the imaging is inconvenient for a doctor to view.
Due to the defects of the image acquisition equipment or poor image acquisition environment, the imaging quality of the endoscope is poor, and under the condition that the image acquisition equipment and the image acquisition environment cannot be changed, the obtained image can be further processed, so that the original image which is not clear enough is restored, or certain characteristic information in the image is highlighted for processing.
Based on the above, the invention provides an endoscope multi-focus image fusion method, which mainly solves the technical problems of small imaging definition range and poor definition of medical images in an image processing mode.
The above information disclosed in this background section is only for enhancement of understanding of the background of the application and therefore it may comprise prior art that does not constitute known to a person of ordinary skill in the art.
Disclosure of Invention
The invention provides an endoscope multi-focus image fusion method for improving the quality of an image acquired and output by an endoscope, aiming at the technical problem of poor imaging quality of the endoscope caused by the defects of image acquisition equipment or poor image acquisition environment in the prior art.
In order to realize the purpose of the invention, the invention is realized by adopting the following technical scheme:
an endoscopic multifocal image fusion method comprising:
an image acquisition step, wherein images are acquired under different focal lengths respectively to obtain a plurality of original images;
a characteristic point extraction step, which is to respectively extract the characteristic points of each original image;
a characteristic matching step, namely performing characteristic matching between every two original images according to the characteristic point pairs to obtain matching point pairs between every two original images;
determining a reference image, and finding out an original image with the most matching points with other original images as the reference image;
correcting all original images to the plane of the reference image to obtain a plurality of corrected images;
a step of light and color homogenizing treatment, which is to respectively carry out light and color homogenizing treatment on the corrected image to obtain a corrected image after light and color homogenizing;
and an image fusion step, namely performing fusion processing on the corrected images after light and color uniformization to obtain a fused image.
Further, in the image correcting step, a homography transformation from other original images except the reference image to the reference image is calculated by adopting a method robust to error matching, and the other original images except the reference image are corrected to the plane of the reference image by using the homography transformation.
Further, the step of light and color homogenizing treatment comprises the following steps:
dividing the plane of the reference image into a plurality of image blocks;
calculating the gray level histogram of each corrected image in the image block in r, g and b channels
Figure 225140DEST_PATH_IMAGE001
，
Figure 405586DEST_PATH_IMAGE002
Wherein k is the number of the corrected image, and p and q represent the row number and the column number of the image block;
calculating the sum of the gray level histograms of all corrected images in the image block at r, g and b channels respectively, and recording the sum as
Figure 864249DEST_PATH_IMAGE003
，
Figure 268685DEST_PATH_IMAGE004
；
Computing each within the image block
Figure 55376DEST_PATH_IMAGE001
，
Figure 570671DEST_PATH_IMAGE002
Are respectively to
Figure 883840DEST_PATH_IMAGE003
，
Figure 459178DEST_PATH_IMAGE004
And obtaining transform coefficients;
and respectively carrying out light and color evening calculation on each corrected image according to the transformation coefficient.
Further, within the image block
Figure 733165DEST_PATH_IMAGE001
，
Figure 881511DEST_PATH_IMAGE002
Are respectively to
Figure 986871DEST_PATH_IMAGE003
，
Figure 670793DEST_PATH_IMAGE004
Linear transformation of (a):
Figure 556709DEST_PATH_IMAGE005
；
Figure 882648DEST_PATH_IMAGE006
；
Figure 842514DEST_PATH_IMAGE007
；
a and b are transform coefficients, respectively.
Further, the method for homogenizing light and color of each corrected image comprises the following steps:
Figure 821972DEST_PATH_IMAGE008
；
Figure 132867DEST_PATH_IMAGE009
；
Figure 731339DEST_PATH_IMAGE010
；
Figure 873607DEST_PATH_IMAGE011
；
Figure 227228DEST_PATH_IMAGE012
which represents the correction of the image or images,
Figure 697524DEST_PATH_IMAGE013
representing the corrected image after the dodging and the evening, (i, j) representing the pixel coordinates,
Figure 162003DEST_PATH_IMAGE014
representing image blocks
Figure 391734DEST_PATH_IMAGE015
Of (2) centerThe coordinates of the pixels are then compared to the coordinates of the pixels,
Figure 916256DEST_PATH_IMAGE016
as a weight value, the weight value,
Figure 873848DEST_PATH_IMAGE017
representing a center-distance pixel of a corrected image block (p, q)
Figure 876439DEST_PATH_IMAGE018
The distance of (a) to (b),
Figure 727720DEST_PATH_IMAGE019
is the size of the image block or blocks,
Figure 423144DEST_PATH_IMAGE020
is the number of rows of the image block corresponding to the ith row of the corrected image,
Figure 868032DEST_PATH_IMAGE021
and correcting the number of columns of the image block corresponding to the jth column of the image.
Further, in the image fusion step, the corrected images after light and color homogenizing are fused by adopting a weighted sum method.
Further, the fusion processing method comprises the following steps:
and (3) calculating the contrast of each pixel in the corrected image after the light and color are homogenized by using a Laplace operator:
Figure 736630DEST_PATH_IMAGE022
；
and (3) calculating the saturation of each pixel by using the standard deviation of three channels of r, g and b after dodging and color evening:
Figure 645681DEST_PATH_IMAGE023
；
calculating a weight value of each pixel using the contrast and the saturation
Figure 184109DEST_PATH_IMAGE024
：
Figure 178610DEST_PATH_IMAGE025
；
Will be provided with
Figure 585321DEST_PATH_IMAGE026
Normalized to obtain
Figure 817719DEST_PATH_IMAGE027
：
Figure 153148DEST_PATH_IMAGE028
；
By using
Figure 838207DEST_PATH_IMAGE027
And (3) fusing the corrected images after light homogenizing and color homogenizing:
Figure 314188DEST_PATH_IMAGE029
；
Figure 135513DEST_PATH_IMAGE030
；
Figure 343640DEST_PATH_IMAGE031
；
n is the number of corrected images after light evening and color evening,
Figure 109471DEST_PATH_IMAGE032
is a fused image.
Further, a gray histogram
Figure 795667DEST_PATH_IMAGE001
，
Figure 471499DEST_PATH_IMAGE033
,
Figure 584949DEST_PATH_IMAGE034
And
Figure 103655DEST_PATH_IMAGE003
，
Figure 531225DEST_PATH_IMAGE035
respectively normalized grey level histograms.
Further, any one of a SIFT algorithm, an HOG algorithm and an ORB algorithm is adopted in the feature point extraction step.
Further, in the step of feature matching, a correlation coefficient feature matching method or a Euclidean distance feature matching method is adopted for feature matching.
Compared with the prior art, the invention has the advantages and positive effects that:
according to the endoscope multi-focus image fusion method, the collected images with different focus distances are fused to obtain an image containing clear positions of all the collected images, and a user can observe the image conveniently.
Compared with a method for directly weighting and summing a plurality of images, the method effectively eliminates the plaque effect, enables the hue and the brightness of the fused image to be more consistent, and enables the whole image to be more natural.
Compared with a pyramid or wavelet transform fusion method, the method has the advantages that the fusion image plaque effect is eliminated, and meanwhile, the calculation amount is remarkably reduced.
Other features and advantages of the present invention will become more apparent from the following detailed description of the invention when taken in conjunction with the accompanying drawings.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flowchart of an embodiment of an endoscopic multi-focus image fusion method according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example one
The present embodiment proposes an endoscope multi-focus image fusion method, as shown in fig. 1, including:
and an image acquisition step, wherein images are acquired under different focal lengths respectively to obtain a plurality of original images.
Firstly, an endoscope is used for collecting images at different focal distances, and a point feature extraction method is used for extracting feature points of each image to obtain feature description of the feature points.
And a characteristic point extraction step, which is to respectively extract the characteristic points of each original image.
Methods that can be used to extract features include, but are not limited to, SIFT algorithm, HOG algorithm, ORB algorithm, and the like.
And a characteristic matching step, namely performing characteristic matching between every two original images according to the characteristic point pairs to obtain matching point pairs between every two original images.
In the feature matching step, a correlation coefficient feature matching method or a euclidean distance feature matching method can be adopted for feature matching.
And determining a reference image, and finding out the original image with the most matching points with other original images as the reference image.
And correcting all the original images to the plane of the reference image to obtain a plurality of corrected images.
And a step of light and color homogenizing treatment, which is to respectively carry out light and color homogenizing treatment on the corrected image to obtain the corrected image after light and color homogenizing.
And an image fusion step, namely performing fusion processing on the corrected images after light and color uniformization to obtain a fused image.
According to the endoscope multi-focus image fusion method, the condition that the endoscope collects a plurality of images with different focus distances is improved, a plurality of clear images at different positions are obtained, and the observation of a user is facilitated.
Compared with a method for directly weighting and summing a plurality of images, the method effectively eliminates the plaque effect, enables the hue and the brightness of the fused image to be more consistent, and enables the whole image to be more natural.
Compared with a pyramid or wavelet transform fusion method, the method has the advantages that the fusion image plaque effect is eliminated, and meanwhile, the calculation amount is remarkably reduced.
In some embodiments, in the image correcting step, homographic transformation of other original images than the reference image to the reference image is calculated by using a method robust to error matching, and the other original images than the reference image are corrected to the plane of the reference image by using the homographic transformation. Through the step, the corrected image homonymous points have the same texture coordinates, and the homonymous points or homonymous areas can be conveniently and quickly found through uniform light and uniform color histogram statistics and final image fusion.
In some embodiments, the RANSAC algorithm may be used to calculate a homographic transformation of the other image to the reference image, and the homographic transformation may be used to correct the other image to the plane of the reference image.
In some embodiments, the step of homogenizing comprises:
the plane of the reference image is divided into a plurality of image blocks.
In order to increase the calculation speed, in some embodiments, the plane of the reference image is divided into a plurality of image blocks with the same size.
Calculating the gray histogram of each corrected image in the image block in r, g and b channels
Figure 950312DEST_PATH_IMAGE001
，
Figure 703504DEST_PATH_IMAGE033
,
Figure 381610DEST_PATH_IMAGE034
Where k is the number of the correction image, and p and q denote the row number and column number of the image block.
Calculating the sum of the gray level histograms of all corrected images in the image block at r, g and b channels, and recording the sum as
Figure 471926DEST_PATH_IMAGE036
Figure 919088DEST_PATH_IMAGE003
，
Figure 577602DEST_PATH_IMAGE035
Figure 743004DEST_PATH_IMAGE037
。
Computing each within the image block
Figure 902590DEST_PATH_IMAGE001
，
Figure 141942DEST_PATH_IMAGE033
,
Figure 361570DEST_PATH_IMAGE034
Are respectively to
Figure 14269DEST_PATH_IMAGE003
，
Figure 321753DEST_PATH_IMAGE035
And obtaining transform coefficients.
And respectively carrying out light and color evening calculation on each corrected image according to the transformation coefficient.
Through linear transformation, the brightness and the tone of the image after light and color uniformization are basically consistent, and the plaque effect caused by the color or the brightness of the fused image is avoided.
In some embodimentsWithin image blocks
Figure 743507DEST_PATH_IMAGE001
，
Figure 369923DEST_PATH_IMAGE033
,
Figure 244338DEST_PATH_IMAGE034
Are respectively to
Figure 621093DEST_PATH_IMAGE003
，
Figure 897353DEST_PATH_IMAGE035
Linear transformation of (a):
Figure 193205DEST_PATH_IMAGE005
；
Figure 554917DEST_PATH_IMAGE006
；
Figure 204204DEST_PATH_IMAGE007
；
a and b are transform coefficients, respectively.
The transform coefficients of the same image in one image block have the same value of a and the same value of b. The r, g and b channel a values in the same image block are more stable than the a value.
In some embodiments, the method of homogenizing the respective correction images is:
Figure 600550DEST_PATH_IMAGE008
；
Figure 67304DEST_PATH_IMAGE009
；
Figure 916311DEST_PATH_IMAGE010
；
Figure 369289DEST_PATH_IMAGE011
；
Figure 948038DEST_PATH_IMAGE012
which represents the correction of the image or images,
Figure 523376DEST_PATH_IMAGE013
representing the corrected image after the dodging and the evening, (i, j) representing the pixel coordinates,
Figure 797362DEST_PATH_IMAGE014
representing image blocks
Figure 116348DEST_PATH_IMAGE015
The coordinates of the center pixel of (a),
Figure 805997DEST_PATH_IMAGE016
as a weight value, the weight value,
Figure 552236DEST_PATH_IMAGE017
representing a center-distance pixel of a corrected image block (p, q)
Figure 47939DEST_PATH_IMAGE018
The distance of (a) to (b),
Figure 170616DEST_PATH_IMAGE019
is the size of the image block or blocks,
Figure 192799DEST_PATH_IMAGE020
is the number of rows of the image block corresponding to the ith row of the corrected image,
Figure 313202DEST_PATH_IMAGE021
and correcting the number of columns of the image block corresponding to the jth column of the image.
Figure 624097DEST_PATH_IMAGE016
The transformation parameters representing the image block (p, q) influence the magnitude of the gray value at (i, j). The further an image block (p, q) is from a pixel (i, j), the smaller the impact.
In some embodiments, in the image fusion step, the corrected image after dodging and evening is subjected to fusion processing by adopting a weighted sum method. And obtaining a fused image containing clear positions of the plurality of images.
In some embodiments, the fusion processing method is:
and (3) calculating the contrast of each pixel in the corrected image after the light and color are homogenized by using a Laplace operator:
Figure 347203DEST_PATH_IMAGE022
。
the image fusion step can distribute higher weight to pixels with high saturation and high contrast, so that the fused image comprises the positions of clear imaging and high saturation of each image.
In some embodiments, the 3 × 3 laplacian operator can be used to calculate the contrast of each pixel in the homogenized corrected image.
And (3) calculating the saturation of each pixel by using the standard deviation of three channels of r, g and b after dodging and color evening:
Figure 161575DEST_PATH_IMAGE023
；
calculating a weight value of each pixel using the contrast and the saturation
Figure 452879DEST_PATH_IMAGE024
：
Figure 985491DEST_PATH_IMAGE025
；
Will be provided with
Figure 246708DEST_PATH_IMAGE026
Normalized to obtain
Figure 181166DEST_PATH_IMAGE027
：
Figure 643372DEST_PATH_IMAGE028
；
By using
Figure 397701DEST_PATH_IMAGE027
And (3) fusing the corrected images after light homogenizing and color homogenizing:
Figure 495232DEST_PATH_IMAGE029
；
Figure 221880DEST_PATH_IMAGE030
；
Figure 651724DEST_PATH_IMAGE031
；
n is the number of corrected images after light evening and color evening,
Figure 221246DEST_PATH_IMAGE032
is a fused image.
In some embodiments, a grayscale histogram
Figure 27528DEST_PATH_IMAGE038
Figure 608682DEST_PATH_IMAGE001
，
Figure 475007DEST_PATH_IMAGE033
,
Figure 266245DEST_PATH_IMAGE034
And
Figure 876218DEST_PATH_IMAGE003
，
Figure 577458DEST_PATH_IMAGE035
respectively normalized grey level histograms.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions.

Claims (10)

1. An endoscopic multifocal image fusion method characterized by comprising:
an image acquisition step, wherein images are acquired under different focal lengths respectively to obtain a plurality of original images;
a characteristic point extraction step, which is to respectively extract the characteristic points of each original image;
a characteristic matching step, namely performing characteristic matching between every two original images according to the characteristic point pairs to obtain matching point pairs between every two original images;
determining a reference image, and finding out an original image with the most matching points with other original images as the reference image;
correcting all original images to the plane of the reference image to obtain a plurality of corrected images;
a step of light and color homogenizing treatment, which is to respectively carry out light and color homogenizing treatment on the corrected image to obtain a corrected image after light and color homogenizing;
and an image fusion step, namely performing fusion processing on the corrected images after light and color uniformization to obtain a fused image.
2. The endoscopic multifocal image fusion method according to claim 1, characterized in that in the image correction step, homographic transformations of other original images than the reference image to the reference image are calculated by a method robust to mismatching, and the homographic transformations are used to correct the other original images than the reference image to the plane of the reference image.
3. The endoscopic multifocal image fusion method according to claim 1, characterized in that the dodging and color-homogenizing process step comprises:
dividing the plane of the reference image into a plurality of image blocks;
calculating the gray level histogram of each corrected image in the image block in r, g and b channels,
Figure 630749DEST_PATH_IMAGE001
,
Figure 16731DEST_PATH_IMAGE002
wherein k is the number of the corrected image, and p and q represent the row number and the column number of the image block;
calculating the sum of the gray level histograms of all corrected images in the image block at r, g and b channels respectively, and recording the sum as
Figure 62047DEST_PATH_IMAGE003
，
Figure 785153DEST_PATH_IMAGE004
；
Computing each within the image block
Figure 865104DEST_PATH_IMAGE005
，
Figure 890829DEST_PATH_IMAGE001
,
Figure 423441DEST_PATH_IMAGE002
Are respectively to
Figure 950238DEST_PATH_IMAGE003
，
Figure 884696DEST_PATH_IMAGE004
And obtaining transform coefficients;
and respectively carrying out light and color evening calculation on each corrected image according to the transformation coefficient.
4. The endoscopic multifocal image fusion method according to claim 3, characterized in that within said image block
Figure 81322DEST_PATH_IMAGE005
，
Figure 101230DEST_PATH_IMAGE001
,
Figure 166138DEST_PATH_IMAGE002
Are respectively to
Figure 955103DEST_PATH_IMAGE003
，
Figure 588210DEST_PATH_IMAGE004
The linear transformation method of (3) is as follows:
Figure 95414DEST_PATH_IMAGE006
；
Figure 196969DEST_PATH_IMAGE007
；
Figure 106019DEST_PATH_IMAGE008
；
a and b are transform coefficients, respectively.
5. An endoscope multifocal image fusion method according to claim 4, characterized in that the method of homogenizing the respective correction images comprises:
Figure 644448DEST_PATH_IMAGE009
；
Figure 638949DEST_PATH_IMAGE010
；
Figure 45659DEST_PATH_IMAGE011
；
Figure 809216DEST_PATH_IMAGE012
；
Figure 784125DEST_PATH_IMAGE013
which represents the correction of the image or images,
Figure 343DEST_PATH_IMAGE014
representing the corrected image after the dodging and the evening, (i, j) representing the pixel coordinates,
Figure 210744DEST_PATH_IMAGE015
representing image blocks
Figure 828808DEST_PATH_IMAGE016
The coordinates of the center pixel of (a),
Figure 974618DEST_PATH_IMAGE017
as a weight value, the weight value,
Figure 6028DEST_PATH_IMAGE018
representing a center-distance pixel of a corrected image block (p, q)
Figure 692224DEST_PATH_IMAGE019
The distance of (a) to (b),
Figure 368056DEST_PATH_IMAGE020
is the size of the image block or blocks,
Figure 747085DEST_PATH_IMAGE021
is the number of rows of the image block corresponding to the ith row of the corrected image,
Figure 501677DEST_PATH_IMAGE022
and correcting the number of columns of the image block corresponding to the jth column of the image.
6. An endoscope multifocal image fusion method according to claim 5, characterized in that in the image fusion step, the corrected image after the dodging and the color evening is fused by a weighted sum method.
7. The endoscopic multifocal image fusion method according to claim 6, characterized in that the fusion processing method is: and (3) calculating the contrast of each pixel in the corrected image after the light and color are homogenized by using a Laplace operator:
Figure 991564DEST_PATH_IMAGE023
；
and (3) calculating the saturation of each pixel by using the standard deviation of three channels of r, g and b after dodging and color evening:
Figure 256323DEST_PATH_IMAGE024
；
calculating a weight value of each pixel using the contrast and the saturation
Figure 806253DEST_PATH_IMAGE025
：
Figure 546676DEST_PATH_IMAGE026
；
Will be provided with
Figure 840254DEST_PATH_IMAGE027
Normalized to obtain
Figure 225099DEST_PATH_IMAGE028
：
Figure 680351DEST_PATH_IMAGE029
；
By using
Figure 908070DEST_PATH_IMAGE028
And (3) fusing the corrected images after light homogenizing and color homogenizing:
Figure 739760DEST_PATH_IMAGE030
；
Figure 244691DEST_PATH_IMAGE031
；
Figure 870844DEST_PATH_IMAGE032
；
n is the number of corrected images after light evening and color evening,
Figure 585859DEST_PATH_IMAGE033
is a fused image.
8. The endoscopic multifocal image fusion method according to claim 3, characterized in that the grey level histogram
Figure 221240DEST_PATH_IMAGE005
，
Figure 315098DEST_PATH_IMAGE034
And
Figure 377732DEST_PATH_IMAGE003
，
Figure 812999DEST_PATH_IMAGE035
Figure 252070DEST_PATH_IMAGE036
Figure 466014DEST_PATH_IMAGE037
，
Figure 699549DEST_PATH_IMAGE038
respectively normalized grey level histograms.
9. The endoscopic multifocal image fusion method according to any of claims 1 to 8, characterized in that any of a SIFT algorithm, a HOG algorithm, and an ORB algorithm is used in the feature point extraction step.
10. The endoscope multifocal image fusion method according to any of claims 1 to 8, characterized in that in the feature matching step, feature matching is performed by using a correlation coefficient feature matching method or an Euclidean distance feature matching method, and a RANSAC method is used to filter matching results.
CN202210320964.9A 2022-03-30 2022-03-30 Endoscope multi-focus image fusion method Active CN114418920B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210320964.9A CN114418920B (en) 2022-03-30 2022-03-30 Endoscope multi-focus image fusion method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210320964.9A CN114418920B (en) 2022-03-30 2022-03-30 Endoscope multi-focus image fusion method

Publications (2)

Publication Number Publication Date
CN114418920A true CN114418920A (en) 2022-04-29
CN114418920B CN114418920B (en) 2022-06-28

Family

ID=81263403

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210320964.9A Active CN114418920B (en) 2022-03-30 2022-03-30 Endoscope multi-focus image fusion method

Country Status (1)

Country Link
CN (1) CN114418920B (en)

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103226822A (en) * 2013-05-15 2013-07-31 清华大学 Medical image stitching method
CN104182949A (en) * 2014-08-18 2014-12-03 武汉大学 Image inking and fusing method and system based on histogram feature point registration
US20150055886A1 (en) * 2013-08-20 2015-02-26 Samsung Techwin Co., Ltd. Image fusion system and method
CN106504194A (en) * 2016-11-03 2017-03-15 重庆邮电大学 A kind of image split-joint method based on most preferably splicing plane and local feature
CN107016647A (en) * 2017-04-26 2017-08-04 武汉大学 Panoramic picture color tone consistency correcting method and system
CN109190460A (en) * 2018-07-23 2019-01-11 南京航空航天大学 Based on cumulative matches and etc. error rates hand shape arm vein fusion identification method
KR102235127B1 (en) * 2019-12-18 2021-04-01 세종대학교산학협력단 Adaptive fusion method of multimodal images and apparatus
CN113570536A (en) * 2021-07-31 2021-10-29 中国人民解放军61646部队 Panchromatic and multispectral image real-time fusion method based on CPU and GPU cooperative processing

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103226822A (en) * 2013-05-15 2013-07-31 清华大学 Medical image stitching method
US20150055886A1 (en) * 2013-08-20 2015-02-26 Samsung Techwin Co., Ltd. Image fusion system and method
CN104182949A (en) * 2014-08-18 2014-12-03 武汉大学 Image inking and fusing method and system based on histogram feature point registration
CN106504194A (en) * 2016-11-03 2017-03-15 重庆邮电大学 A kind of image split-joint method based on most preferably splicing plane and local feature
CN107016647A (en) * 2017-04-26 2017-08-04 武汉大学 Panoramic picture color tone consistency correcting method and system
CN109190460A (en) * 2018-07-23 2019-01-11 南京航空航天大学 Based on cumulative matches and etc. error rates hand shape arm vein fusion identification method
KR102235127B1 (en) * 2019-12-18 2021-04-01 세종대학교산학협력단 Adaptive fusion method of multimodal images and apparatus
CN113570536A (en) * 2021-07-31 2021-10-29 中国人民解放军61646部队 Panchromatic and multispectral image real-time fusion method based on CPU and GPU cooperative processing

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
CODRUTA O. ANCUTI ET AL: "Color Balance and Fusion for Underwater Image Enhancement", 《IEEE TRANSACTIONS ON IMAGE PROCESSING》 *
俞家勇: "移动测量系统异构数据高精度融合方法研究", 《中国博士学位论文全文数据库（电子期刊）》 *
柴庆朋: "面向图像拼接的几何校准和颜色校正技术研究", 《中国优秀硕士学位论文全文数据库（电子期刊）》 *

Also Published As

Publication number Publication date
CN114418920B (en) 2022-06-28

Similar Documents

Publication Publication Date Title
US20220207728A1 (en) Quality assessment in video endoscopy
JP5094036B2 (en) Endoscope insertion direction detection device
US20220157047A1 (en) Feature Point Detection
JP4104054B2 (en) Image alignment apparatus and image processing apparatus
CN112804943B (en) Learning completion model creation method, brightness adjustment method, and image processing apparatus
JP2010512173A (en) Computer-aided analysis using video from an endoscope
US8666135B2 (en) Image processing apparatus
CN112446880B (en) Image processing method, electronic device and readable storage medium
CN110751605A (en) Image processing method and device, electronic equipment and readable storage medium
US20220006981A1 (en) Method of automatic image freezing of digestive endoscopy
JP4274400B2 (en) Image registration method and apparatus
CN114004969A (en) Endoscope image focal zone detection method, device, equipment and storage medium
Huang et al. Deep unsupervised endoscopic image enhancement based on multi-image fusion
CN111161852B (en) Endoscope image processing method, electronic equipment and endoscope system
CN114693682B (en) Spine feature identification method based on image processing
CN114418920B (en) Endoscope multi-focus image fusion method
CN112215878A (en) X-ray image registration method based on SURF feature points
CN115223193B (en) Capsule endoscope image focus identification method based on focus feature importance
CN116029934A (en) Low-dose DR image and CT image denoising method
CN114387191B (en) Endoscope image enhancement method and endoscope device
Jimenez-Martin et al. Specular reflections removal in colposcopic images based on neural networks: Supervised training with no ground truth previous knowledge
AU2022230997B2 (en) Method of image enhancement for distraction deduction in endoscopic procedures
Flores et al. Identifying precursory cancer lesions using temporal texture analysis
KR102383495B1 (en) Medical image data extraction method
CN117764847A (en) Method and system for focusing white light image and fluorescent image of 3D endoscope

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant