CN111539967B - Method and system for identifying and processing interference fringe region in terahertz imaging of focal plane - Google Patents
Method and system for identifying and processing interference fringe region in terahertz imaging of focal plane Download PDFInfo
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Abstract
The invention discloses a method and a system for identifying and processing an interference fringe region in focal plane terahertz imaging, wherein the method comprises the following steps: performing global threshold segmentation on an image obtained in the terahertz imaging of the focal plane to obtain a global threshold of the image and obtain a first image; judging whether the first image has uneven brightness: if so, performing local threshold segmentation on the first image to obtain a second image, performing stripe identification on the second image to obtain a dark stripe area of the second image, merging the dark stripe area of the first image and the dark stripe area of the second image, and performing stripe elimination to obtain a third image; otherwise, performing stripe recognition on the first image to obtain a dark stripe area of the first image, and performing stripe elimination to obtain a fourth image. And carrying out image enhancement processing on the third image or the fourth image. The invention can eliminate the interference of large-area interference fringes in the image on the target object and simultaneously can greatly improve the quality of the image.
Description
Technical Field
The invention relates to the field of focal plane terahertz imaging, in particular to a method and a system for identifying and processing an interference fringe region in focal plane terahertz imaging.
Background
Terahertz radiation refers to electromagnetic waves with the frequency of 0.1-10THz, the wave band of the terahertz radiation is between microwave and infrared, and the terahertz radiation belongs to the field of far infrared electromagnetic radiation. With the progress of the terahertz wave source end and the detection end, the continuous terahertz imaging system continuously seeks breakthrough from the directions of small volume, high speed, convenient operation and the like. The terahertz focal plane array detector has the advantages of low cost, convenience in operation, high acquisition speed, high image quality signal-to-noise ratio and the like, and has a very wide application scene.
However, due to the fact that the terahertz wavelength is long, a very obvious interference phenomenon occurs on a light path from a terahertz wave source end to a detection end, so that large-area interference fringes appear in a background of an image collected finally, imaging of a target is greatly influenced, and due to the fact that the terahertz energy is low, the overall contrast of the image is low, and image enhancement needs to be carried out on the image. In the prior art, researchers have made a lot of researches, but most of the researches still use direct high-frequency filtering or band-stop filtering, and only can solve the stripe noise which is strictly and regularly distributed on the whole image, once the interference fringes only locally appear or are slightly disordered, the interference fringes cannot be processed, and the interference of the fringes can be enhanced by the conventional image enhancement method. Therefore, an effective method for identifying and processing the stripes in the terahertz image is needed.
Disclosure of Invention
The invention mainly solves the technical problem of providing a method for identifying stripes in a terahertz image, which can be used for identifying and processing the stripes in the terahertz image in a targeted manner.
In order to solve the technical problems, the invention adopts a technical scheme that: the method for identifying and processing the interference fringe region in the terahertz imaging of the focal plane is provided.
The method comprises the following steps:
carrying out global threshold segmentation on an image obtained in the terahertz imaging of a focal plane to obtain a global threshold of the image and obtain a first image;
judging whether the first image has uneven brightness: if so, carrying out local threshold segmentation on the first image to obtain a second image, and carrying out stripe identification on the second image to obtain a dark stripe area of the second image; otherwise, carrying out stripe recognition on the first image to obtain a dark stripe area of the first image;
if the brightness is uneven, combining the dark stripe area of the first image and the dark stripe area of the second image and carrying out stripe elimination processing to obtain a third image; otherwise, carrying out stripe elimination processing on the dark stripe area of the first image to obtain a fourth image.
And carrying out image enhancement processing on the third image or the fourth image.
Preferably, before performing global threshold segmentation, linear stretching is further performed on an image obtained in the focal plane terahertz imaging. The global thresholding includes using single-thresholding based on the maximum inter-class variance method, OTSU.
Preferably, the determining whether the first image has uneven brightness includes:
calculating the difference degrees of four boundary areas, namely an upper boundary area, a lower boundary area, a left boundary area, a right boundary area and a left boundary area of the first image respectively, wherein the four boundary areas refer to four rectangular areas close to the edge of the first image;
finding the maximum difference degree in the four boundary areas;
and judging whether the maximum difference absolute value is larger than a threshold value or not, wherein the first image has uneven brightness.
Preferably, multiple local threshold segmentation is performed according to the condition of uneven brightness to obtain a fifth image; image enhancement processing the third image or the fourth image includes using an enhancement auxiliary binary image including:
when the brightness unevenness does not exist in the first image, the enhanced auxiliary binary image adopts the first image;
when the first image has uneven brightness, if the local threshold segmentation is performed only once, the enhanced auxiliary binary image adopts a second image; otherwise, the enhanced auxiliary binary image adopts the fifth image.
Preferably, when the maximum difference is a positive value, calculating an average coordinate position of a bright pixel in the first image; and when the maximum difference degree is a negative value, calculating the average coordinate position of the dark pixels in the first image.
Preferably, the stripe recognition includes: obtaining a binary image with better characteristics in the first image, scanning the binary image line by line or column by column according to the stripe direction, and storing the continuous lengths of bright pixel points and dark pixel points into a length section; and judging the part of the dark stripe in the length segment, restoring the part into the pixel position of the original image and marking the pixel position in a marking matrix with the same size as the original image.
Preferably, the stripe recognition further comprises finding similar points within a certain distance around according to the mark points in the mark matrix.
Preferably, the image enhancement processing on the third image or the fourth image includes performing gray scale improvement processing on pixels in a dark stripe region of background pixels of the third image or the fourth image.
Preferably, the third image or the fourth image is subjected to the gray scale enhancement processing and then subjected to noise reduction processing by using a mean filtering method.
An interference fringe region identification and processing system in focal plane terahertz imaging comprises:
a global threshold segmentation module: the method comprises the steps of performing global threshold segmentation on an image obtained in planar terahertz imaging to obtain a global threshold of the image and obtain a first image;
a local threshold segmentation module: judging whether the first image is uneven in brightness: if so, carrying out local threshold segmentation on the first image to obtain a second image, and carrying out stripe identification on the second image to obtain a dark stripe area of the second image; otherwise, carrying out stripe recognition on the first image to obtain a dark stripe area of the first image;
stripe merge and elimination module: if the brightness is not uniform, combining the dark stripe area of the first image and the dark stripe area of the second image and carrying out stripe elimination processing to obtain a third image; otherwise, carrying out stripe elimination processing on the dark stripe area of the first image to obtain a fourth image.
The system for identifying and processing the interference fringe region in the terahertz imaging of the focal plane further comprises:
an image enhancement module: and carrying out image enhancement processing on the third image or the fourth image.
The invention has the beneficial effects that:
(1) Processing the terahertz graph by adopting a mode of combining global threshold segmentation and local threshold segmentation, and automatically identifying areas with uneven brightness and carrying out corresponding processing; meanwhile, the stripe area is accurately identified and the dark stripes are processed, so that the interference of large-area stripes in the image on the target object is eliminated; (2) Obtaining a binary image through local threshold segmentation to perform image enhancement, so that a target object is more obviously shown; (3) Compared with the traditional processing method of high-frequency filtering and some band elimination filters, the method has the advantages of simpler calculation process, better effect, wider application range and huge practical value; (4) An interference fringe region identification and processing system in focal plane terahertz imaging can conduct real-time imaging, avoid the influence of interference fringes to the maximum extent and improve the quality of images.
Drawings
FIG. 1 is a general flow chart of the present invention;
FIG. 2 is a raw image acquired;
FIG. 3 is an image after linear pull-up;
FIG. 4 is a binary image obtained by global threshold image segmentation;
FIG. 5 is a binary image obtained by a single segmentation of a local threshold image when the luminance is not uniform;
FIG. 6 is a binary image resulting from a second local threshold segmentation for image enhancement assistance;
FIGS. 7 and 8 are the results of the striped recognition of FIG. 4 and FIG. 5, respectively, in which the dark portions are dark striped areas;
FIG. 9 is the result of the merging of the dark striped areas of FIGS. 7 and 8;
FIG. 10 is a graph of the effect of dark streaks removed;
fig. 11 is a diagram of the effect after image enhancement.
Detailed Description
In order to better understand the technical scheme of the invention, the technical scheme of the invention is described in detail in the following with the accompanying drawings and specific embodiments.
Example 1
In an exemplary embodiment, a method for identifying and processing an interference fringe region in focal plane terahertz imaging as shown in fig. 1 includes the following steps:
carrying out global threshold segmentation on an image obtained in the terahertz imaging of a focal plane to obtain a global threshold of the image and obtain a first image;
judging whether the first image is uneven in brightness: if so, carrying out local threshold segmentation on the first image to obtain a second image, and carrying out stripe identification on the second image to obtain a dark stripe area of the second image; otherwise, carrying out stripe recognition on the first image to obtain a dark stripe area of the first image;
if the brightness is uneven, combining the dark stripe area of the first image and the dark stripe area of the second image and carrying out stripe elimination processing to obtain a third image; otherwise, carrying out stripe elimination processing on the dark stripe area of the first image to obtain a fourth image.
And carrying out image enhancement processing on the third image or the fourth image.
In the prior art, the difficulty of dividing the focal plane terahertz image is poor contrast, the overall image is fuzzy, the shape is not easy to see clearly, and the image has the influence of stripes, and meanwhile, the illumination distribution is not uniform.
Preferably, before performing global threshold segmentation, linear stretching is further performed on an image obtained in the focal plane terahertz imaging. The global thresholding includes using single-thresholding based on the maximum inter-class variance method, OTSU.
Preferably, the determining whether the first image has uneven brightness includes:
calculating the difference degrees of four boundary areas, namely an upper boundary area, a lower boundary area, a left boundary area, a right boundary area and a left boundary area of the first image respectively, wherein the four boundary areas refer to four rectangular areas close to the edge of the first image;
finding the maximum difference degree in the four boundary areas;
and judging whether the maximum difference absolute value is larger than a threshold value or not, wherein the first image has uneven brightness.
Further, the difference degrees of the four boundary areas of the upper, lower, left and right sides of the image are respectively calculated when the global threshold is respectively added or subtracted by 20. In an image of size M × N, four boundary regions are, respectively, 0-N x-here M/10, 0-y-once N/10,0.9 × M < x < M,0.9 × N < y < N, and the degree of difference P is calculated by:
wherein l 1 The number of bright pixels after area binarization is given as the global threshold plus 20, d2 is the number of dark pixels after area binarization is given as the global threshold minus 20, and l and d are the numbers of bright and dark pixels without change in the threshold.
Further, whether the first image has uneven brightness further comprises finding the maximum difference degree in the four boundary areas, and when the absolute value of the maximum difference degree is greater than 0.5, the first image has uneven brightness.
Further, according to the uneven brightness, multiple local threshold value divisions are performed. And when the local threshold segmentation is carried out in the S2, the threshold adjustment is carried out on the global threshold in the S1.
Further, when the maximum difference degree is a positive value, calculating the average coordinate position of the bright pixels in the first imagep 0 (ii) a And when the maximum difference degree is a negative value, calculating the average coordinate position of the dark pixels in the first image. Note that the dark pixel coordinate position is D (a, b), let D to p 0 The threshold value in the vertical direction of the original value area U is adjusted.
When this direction is horizontal, the formula for the adjustment is as follows:
when this direction is vertical, the formula for the adjustment is as follows:
wherein T is the adjusted threshold value, T 0 For the global threshold, Q is the magnitude of the adjustment, L =0 when horizontally left or vertically up, L = M when horizontally right, L = N when vertically down, M, N being the image length and width.
Further, performing multiple local threshold segmentation according to the condition of uneven brightness to obtain a fifth image; image enhancement processing the third image or the fourth image includes using an enhancement auxiliary binary image including:
when the brightness unevenness does not exist in the first image, the enhanced auxiliary binary image adopts the first image;
when the first image has uneven brightness, if the local threshold segmentation is performed only once, the enhanced auxiliary binary image adopts a second image; otherwise, the enhanced auxiliary binary image adopts the fifth image.
Further, if the difference degree p of the reverse region of U 1 When the absolute value of (A) is more than 0.3, the process from D to p is carried out once 1 The local threshold adjustment process. The obtained binary image is used for enhancing an auxiliary binary image in the image enhancement process.
Further, the stripe recognition includes: obtaining a binary image with better characteristics in the first image, scanning the binary image line by line or line by line according to the stripe direction, and storing the continuous lengths of bright pixel points and dark pixel points into a length section; and judging the part of the dark stripe in the length segment, restoring the part into the pixel position of the original image and marking the pixel position in a marking matrix with the same size as the original image.
Further, the stripe recognition also comprises finding similar points within a certain distance around according to the mark points in the mark matrix.
Further, the image enhancement processing on the third image comprises performing gray scale improvement processing on pixels in a background pixel dark stripe region of the third image.
Further, after the gray level improvement processing is performed on the third image, a mean value filtering mode is used for noise reduction processing.
Example 2
An interference fringe region identification and processing system in focal plane terahertz imaging comprises:
a global threshold segmentation module: the method comprises the steps of performing global threshold segmentation on an image obtained in planar terahertz imaging to obtain a global threshold of the image and obtain a first image;
a local threshold segmentation module: judging whether the first image is uneven in brightness: if so, carrying out local threshold segmentation on the first image to obtain a second image, and carrying out stripe identification on the second image to obtain a dark stripe area of the second image; otherwise, carrying out stripe recognition on the first image to obtain a dark stripe area of the first image;
a stripe merge and elimination module: if the brightness is uneven, combining the dark stripe area of the first image and the dark stripe area of the second image and carrying out stripe elimination processing to obtain a third image; otherwise, carrying out stripe elimination processing on the dark stripe area of the first image to obtain a fourth image.
An image enhancement module: and carrying out image enhancement processing on the third image or the fourth image.
Specifically, in an exemplary embodiment, after the small focal plane terahertz detector is used to scan the imaging system to image the blade, the original image shown in fig. 2 is obtained, it can be seen that the right half portion of the image has regular light and dark alternate stripes, which are caused by interference in the light path from the terahertz wave source end to the detection end, and meanwhile, the image contrast is low, and it is difficult to identify the blade in the image. The invention provides a method for identifying and processing an interference fringe region in focal plane terahertz imaging, a flow chart is shown in fig. 1, and the method comprises the following steps:
linear pull-up process: the contrast ratio is improved by using histogram linear pull-up for fig. 2, which can improve the accuracy of threshold segmentation to some extent, and the result is shown in fig. 3.
Global threshold segmentation process: for fig. 3, a binary image capable of better representing the stripes and the object features is obtained by using single threshold image segmentation based on the maximum inter-class variance (OTSU), as shown in fig. 4, a black part ideally should include an actual imaging target and dark stripes, and a white part is a background. It is also desirable to preserve the global threshold for subsequent local threshold segmentation and streak removal.
Local threshold segmentation process: judging whether a new local threshold segmentation method is used or not according to whether brightness unevenness occurs or not to obtain an improved binary image, wherein as shown in fig. 5, the new binary image can segment the target and the background at the lower half part of the image, so that the difference between the stripe and the blade as well as the background is better shown; as a result of the quadratic use of local thresholding required for image enhancement, the actual shape of the entire blade can be clearly seen, as shown in fig. 6.
The method for determining uneven brightness specifically includes calculating the difference degrees of upper, lower, left and right boundary regions of the whole image under the condition that global thresholds are respectively increased or decreased by 20, in an image with the size of M × N, the four boundary regions are respectively 0-once x-once M/10, 0-once y-once N/10,0.9 × M < x < M,0.9 × N < y < N, and the calculation method of the difference degree P is as follows:
wherein l 1 The number of bright pixels after area binarization is given as the global threshold plus 20, d2 is the number of dark pixels after area binarization is given as the global threshold minus 20, and l and d are the numbers of bright and dark pixels without change in the threshold. And finding the maximum difference degree in the four areas, and judging that the image has the phenomenon of uneven brightness when the absolute value of the maximum difference degree is more than 0.5.
The local threshold segmentation method specifically includes, in fig. 4 obtained by global threshold segmentation, calculating an average coordinate position p of a bright pixel when the maximum disparity is a positive value 0 Otherwise, calculating the average coordinate position of the dark pixels, recording the coordinate position as D (a, b), and converting D to p 0 The threshold value in the vertical direction of the original value area U is adjusted.
When this direction is horizontal, the formula for the adjustment is as follows:
when this direction is vertical, the formula for the adjustment is as follows:
wherein T is the adjusted threshold value, T 0 For the global threshold, Q is the magnitude of the adjustment, L =0 when horizontally left or vertically up, L = M when horizontally right, L = N when vertically down, M, N being the image length and width.
In particular, if the difference p of the inverse region of U is desired to obtain the binary image required in the image enhancement step 1 When the absolute value of (A) is more than 0.3, the process from D to p is carried out once 1 The local threshold adjustment process.
And (3) stripe identification process: and determining an interference area from the width, the direction and the series of the light and dark fringes according to the interference rule of light in the binary image, and identifying the dark fringe part. As can be seen from fig. 3, the fringes on the right side of the blade clearly follow the law of interference fringes. Fig. 7 and 8 show the results of the stripe recognition in fig. 5 and 6, respectively, and the black portions are the interference dark stripe portions recognized in the original image. The identification method specifically comprises the following steps:
the method comprises the steps of firstly, obtaining a binary image with good characteristics from a first image, scanning the binary image line by line or column by column according to the stripe direction, and storing the continuous lengths of bright pixel points and dark pixel points into a length section.
And secondly, judging the part of the dark fringe in the length section according to the space distribution rule that the width and the distance of the light and dark fringes in the interference fringes of the light are approximately same and the fringe levels are generally more, reducing the part of the dark fringe into the pixel position of the original image and marking the pixel position in a marking matrix with the same size as the original image.
And thirdly, finding similar points within a certain distance around according to the mark points in the mark matrix, wherein the similar points have the characteristic of being similar to and continuous with the gray value of the mark points, so that most areas of the dark stripes can be identified.
And (3) region merging process: the dark fringe areas obtained from the binary images of the global threshold segmentation and the local threshold segmentation are merged, and the merged fringe areas basically cover the actual dark fringe areas as shown in fig. 9.
And (3) strip elimination process: and adjusting the gray value of the combined dark stripe area through a global threshold and a local threshold, thereby eliminating the influence of the stripes. The formula for adjusting the gray value of the fringe area is as follows: g = T 1 +T 2 -G 0 Where T1 is the global threshold of the image, T2 is the local threshold of the image, G 0 Is the gray value of the original image; g is the transformed gray value. The final result is shown in fig. 10, and it can be seen that the influence of the interference fringes on the image is basically eliminated, but due to the problem of low contrast of the terahertz image, the image needs to be further processed.
And (3) image enhancement process: because the quality of the focal plane terahertz image is poor, the noise is serious and complex, the traditional linear pull-up and histogram equalization can not achieve an ideal effect, and the image contrast is improved by innovatively combining a local threshold segmentation result. And carrying out gray level improvement on the pixels in the dark fringe area of the background pixels of the third image, reducing the gray level of the rest parts, and finally reducing noise through mean value filtering. The final result is shown in fig. 11, the fringe influence is greatly eliminated, the actual shape of the blade can be clearly seen in the image, and the visual effect of people is greatly improved, which cannot be obtained by all filtering methods.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes performed by the present specification and drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (9)
1. A method for identifying and processing interference fringe regions in focal plane terahertz imaging is characterized by comprising the following steps: the method comprises the following steps:
carrying out global threshold segmentation on an image obtained in the terahertz imaging of a focal plane to obtain a global threshold of the image and obtain a first image;
judging whether the first image has uneven brightness: if so, carrying out local threshold segmentation on the first image to obtain a second image, and carrying out stripe identification on the second image to obtain a dark stripe area of the second image; otherwise, carrying out stripe recognition on the first image to obtain a dark stripe area of the first image; the judging whether the first image has uneven brightness comprises the following steps:
calculating the difference degrees of four boundary areas, namely an upper boundary area, a lower boundary area, a left boundary area, a right boundary area and a left boundary area of the first image respectively, wherein the four boundary areas refer to four rectangular areas close to the edge of the first image;
finding the maximum difference degree in the four boundary areas;
judging whether the maximum difference absolute value is larger than a threshold value or not, wherein the first image has uneven brightness;
performing multiple local threshold segmentation according to the condition of uneven brightness; performing global thresholding when performing local thresholdingAdjusting a line threshold, and calculating the average coordinate position p of bright pixels in the first image when the maximum difference degree is a positive value 0 (ii) a When the maximum difference degree is a negative value, calculating the average coordinate position of dark pixels in the first image;
note that the dark pixel coordinate position is D (a, b), let D to p 0 Adjusting the threshold value in the vertical direction of the original value area U;
when this direction is horizontal, the formula for the adjustment is as follows:
when this direction is vertical, the formula for the adjustment is as follows:
wherein T is the adjusted threshold value, T 0 For the global threshold, Q is the magnitude of the adjustment, L =0 when horizontally left or vertically up, L = M when horizontally right, L = N when vertically down, M, N being the image length and width;
if the brightness is uneven, combining the dark stripe area of the first image and the dark stripe area of the second image and carrying out stripe elimination processing to obtain a third image; otherwise, carrying out stripe elimination processing on the dark stripe region of the first image to obtain a fourth image;
and carrying out image enhancement processing on the third image or the fourth image.
2. The method for identifying and processing the interference fringe region in the focal plane terahertz imaging according to claim 1, characterized in that: before global threshold segmentation is carried out, linear stretching is further carried out on an image obtained in the terahertz imaging of the focal plane.
3. The method for identifying and processing the interference fringe region in the focal plane terahertz imaging according to claim 1, characterized in that: the global thresholding includes using single-thresholding based on the maximum inter-class variance method, OTSU.
4. The method for identifying and processing the interference fringe region in the focal plane terahertz imaging according to claim 1, characterized in that: performing multiple local threshold segmentation according to the condition of uneven brightness to obtain a fifth image; image enhancement processing the third image or the fourth image includes using an enhancement auxiliary binary image including:
when the brightness unevenness does not exist in the first image, the enhanced auxiliary binary image adopts the first image;
when the first image has uneven brightness, if the local threshold segmentation is performed only once, the enhanced auxiliary binary image adopts a second image; otherwise, the enhanced auxiliary binary image adopts the fifth image.
5. The method for identifying and processing the interference fringe region in the focal plane terahertz imaging according to claim 1, characterized in that: when the maximum difference degree is a positive value, calculating the average coordinate position of the bright pixels in the first image; and when the maximum difference degree is a negative value, calculating the average coordinate position of the dark pixels in the first image.
6. The method for identifying and processing the interference fringe region in the focal plane terahertz imaging according to claim 1, characterized in that: and the image enhancement processing on the third image or the fourth image comprises the step of carrying out gray level improvement processing on the pixels in the dark fringe area of the background pixels of the third image or the fourth image.
7. The method for identifying and processing the interference fringe region in the focal plane terahertz imaging according to claim 6, characterized in that: and performing the gray level improvement processing on the third image or the fourth image, and then performing noise reduction processing by using a mean value filtering mode.
8. The utility model provides an interference fringe region identification and processing system in focal plane terahertz imaging which characterized in that: the system comprises:
a global threshold segmentation module: the method comprises the steps of performing global threshold segmentation on an image obtained in planar terahertz imaging to obtain a global threshold of the image and obtain a first image;
a local threshold segmentation module: judging whether the first image is uneven in brightness: if so, carrying out local threshold segmentation on the first image to obtain a second image, and carrying out stripe identification on the second image to obtain a dark stripe area of the second image; otherwise, carrying out stripe identification on the first image to obtain a dark stripe area of the first image; the judging whether the first image has uneven brightness comprises the following steps:
calculating the difference degrees of four boundary areas, namely an upper boundary area, a lower boundary area, a left boundary area, a right boundary area and a left boundary area of the first image respectively, wherein the four boundary areas refer to four rectangular areas close to the edge of the first image;
finding the maximum difference degree in the four boundary areas;
judging whether the maximum difference absolute value is larger than a threshold value or not, wherein the first image has uneven brightness;
performing multiple local threshold segmentation according to the condition of uneven brightness; performing threshold adjustment on a global threshold during local threshold segmentation, and calculating the average coordinate position p of bright pixels in the first image when the maximum difference is a positive value 0 (ii) a When the maximum difference degree is a negative value, calculating the average coordinate position of dark pixels in the first image;
note that the dark pixel coordinate position is D (a, b), let D to p 0 Adjusting the threshold value in the vertical direction of the original value area U;
when this direction is horizontal, the formula for the adjustment is as follows:
when this direction is vertical, the formula for the adjustment is as follows:
wherein T is the adjusted threshold value, T 0 For the global threshold, Q is the magnitude of the adjustment, L =0 when horizontally left or vertically up, L = M when horizontally right, L = N when vertically down, M, N being the image length and width;
a stripe merge and elimination module: if the brightness is uneven, combining the dark stripe area of the first image and the dark stripe area of the second image and carrying out stripe elimination processing to obtain a third image; otherwise, carrying out stripe elimination processing on the dark stripe area of the first image to obtain a fourth image.
9. The system for identifying and processing the interference fringe region in the focal plane terahertz imaging according to claim 8, is characterized in that: the system further comprises:
an image enhancement module: and carrying out image enhancement processing on the third image or the fourth image.
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Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103940510A (en) * | 2014-04-02 | 2014-07-23 | 中国科学院紫金山天文台 | Device and method for detecting terahertz signal multi-dimensional image through dual fourier transformation |
| CN104517269A (en) * | 2014-12-25 | 2015-04-15 | 深圳市一体太赫兹科技有限公司 | Terahertz image strip processing method and system |
| CN104574300A (en) * | 2014-12-25 | 2015-04-29 | 深圳市一体太赫兹科技有限公司 | Terahertz image noise processing method and system |
| CN107095673A (en) * | 2017-05-24 | 2017-08-29 | 西北核技术研究所 | A kind of reflective real-time tumor imaging method and system |
| CN107481205A (en) * | 2017-08-23 | 2017-12-15 | 电子科技大学 | A kind of Terahertz image fringes noise processing method and system |
| CN108537735A (en) * | 2018-04-16 | 2018-09-14 | 电子科技大学 | A kind of image split-joint method of focal plane terahertz imaging |
| CN108956645A (en) * | 2018-07-18 | 2018-12-07 | 丹阳市精通眼镜技术创新服务中心有限公司 | A kind of the optical mirror slip defect detecting device and method of more vision systems |
| CN109029727A (en) * | 2018-06-11 | 2018-12-18 | 中国科学院紫金山天文台 | Highly sensitive Terahertz superconduction spectrum imaging system and system imaging method based on code aperture |
| CN110196462A (en) * | 2019-06-28 | 2019-09-03 | 苏州晶萃光学科技有限公司 | A kind of Terahertz lens, preparation method and the terahertz optics system comprising it |
| CN110443807A (en) * | 2019-06-27 | 2019-11-12 | 中国地质大学（武汉） | A kind of even carrying out image threshold segmentation method of uneven illumination based on luminance proportion |
| US10568519B1 (en) * | 2014-06-02 | 2020-02-25 | The Spectranetics Corporation | Terahertz scanning system for an intravascular space |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100059677A1 (en) * | 2008-09-10 | 2010-03-11 | Rainer Leonhardt | Aspheric Lenses for Imaging |
-
2020
- 2020-04-24 CN CN202010333623.6A patent/CN111539967B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103940510A (en) * | 2014-04-02 | 2014-07-23 | 中国科学院紫金山天文台 | Device and method for detecting terahertz signal multi-dimensional image through dual fourier transformation |
| US10568519B1 (en) * | 2014-06-02 | 2020-02-25 | The Spectranetics Corporation | Terahertz scanning system for an intravascular space |
| CN104517269A (en) * | 2014-12-25 | 2015-04-15 | 深圳市一体太赫兹科技有限公司 | Terahertz image strip processing method and system |
| CN104574300A (en) * | 2014-12-25 | 2015-04-29 | 深圳市一体太赫兹科技有限公司 | Terahertz image noise processing method and system |
| CN107095673A (en) * | 2017-05-24 | 2017-08-29 | 西北核技术研究所 | A kind of reflective real-time tumor imaging method and system |
| CN107481205A (en) * | 2017-08-23 | 2017-12-15 | 电子科技大学 | A kind of Terahertz image fringes noise processing method and system |
| CN108537735A (en) * | 2018-04-16 | 2018-09-14 | 电子科技大学 | A kind of image split-joint method of focal plane terahertz imaging |
| CN109029727A (en) * | 2018-06-11 | 2018-12-18 | 中国科学院紫金山天文台 | Highly sensitive Terahertz superconduction spectrum imaging system and system imaging method based on code aperture |
| CN108956645A (en) * | 2018-07-18 | 2018-12-07 | 丹阳市精通眼镜技术创新服务中心有限公司 | A kind of the optical mirror slip defect detecting device and method of more vision systems |
| CN110443807A (en) * | 2019-06-27 | 2019-11-12 | 中国地质大学（武汉） | A kind of even carrying out image threshold segmentation method of uneven illumination based on luminance proportion |
| CN110196462A (en) * | 2019-06-28 | 2019-09-03 | 苏州晶萃光学科技有限公司 | A kind of Terahertz lens, preparation method and the terahertz optics system comprising it |
Non-Patent Citations (2)
| Title |
|---|
| Terahertz Imaging Based on Morphological Reconstruction;Jia Shi等;《 IEEE Journal of Selected Topics in Quantum Electronics 》;20170109;第1-7页 * |
| 基于太赫兹量子级联激光器的实时成像研究进展;谭智勇等;《中国光学》;20170215;第68-76页 * |
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