CN113992838A - Imaging focusing method and control method of silicon-based multispectral signal - Google Patents
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Abstract
The invention discloses an imaging focusing method and a control method of a silicon-based multispectral signal, which comprises the following steps: firstly, during shooting, quickly obtaining a clear rough clear multispectral image signal with a small amount of noise through coarse focusing, and shooting a group of full-color images of an object; secondly, enabling the multispectral image signals in the previous step to pass through a denoising module, wherein the denoising module can remove noise of the multispectral image signals, but can weaken the edge characteristics of the image; and thirdly, injecting the edge information of the full-color image into a clear multispectral image signal weakening the edge characteristics of the image so as to obtain a fused image with high-frequency detail information, and thus obtaining a high-definition image. The invention has reasonable design and ingenious conception, and can obtain the image with high-frequency detail information by denoising and injecting the image signal through the high-pass filter and the multispectral edge calculation terminal without fine focusing after coarse focusing.
Description
Technical Field
The invention relates to the technical field of multispectral images, in particular to an imaging focusing method, a control method and a control method of a silicon-based multispectral signal.
Background
Multispectral images are images that contain many bands, sometimes only 3 bands (color images are an example) but sometimes many more, even hundreds. Each band is a grayscale image that represents the brightness of the scene, derived from the sensitivity of the sensor used to create the band. In such an image, each pixel is associated with a string of values, i.e. a vector, in different bands by the pixel. This string is called the spectral signature of the pixel.
However, in the prior art, when a silicon-based multispectral image signal is focused, the positions of a photosensitive device and a molded object of a general camera are fixed, the object distance and the distance are changed by adjusting the position of a lens, and then focusing is realized.
Disclosure of Invention
The invention provides an imaging focusing method, a control method and a control method of a silicon-based multispectral signal, which aim to solve the problems in the background technology.
In order to achieve the purpose, the invention adopts the following technical scheme:
an imaging focusing method and a control method of a silicon-based multispectral signal comprise the following steps: :
firstly, during shooting, quickly obtaining a clear rough clear multispectral image signal with a small amount of noise through coarse focusing, and shooting a group of full-color images of an object;
secondly, enabling the multispectral image signals in the previous step to pass through a denoising module, wherein the denoising module can remove noise of the multispectral image signals, but can weaken the edge characteristics of the image;
and thirdly, injecting the edge information of the full-color image into a clear multispectral image signal weakening the edge characteristics of the image so as to obtain a fused image with high-frequency detail information, and thus obtaining a high-definition image.
As a further improvement scheme of the technical scheme: in the first step, during coarse focusing, an optical lens is rotated to collect images of a target scene at regular intervals, and when the position with the maximum definition is the position of a multispectral image signal with coarse focusing definition, the multispectral image signal with coarse focusing definition is obtained.
As a further improvement scheme of the technical scheme: in the second step, the denoising module is a high-pass filter.
As a further improvement scheme of the technical scheme: in the third step, when the injected edge information is less, the detail information of the fused image is blurred, and when the injected edge information is excessive, the spectrum distortion of the fused image is caused, and the edge information amount of the injected multispectral image signal is controlled by adopting a self-adaptive edge extraction method.
As a further improvement scheme of the technical scheme: in the first step, a multispectral imaging camera is used to photograph the object.
As a further improvement scheme of the technical scheme: and thirdly, injecting edge information of the full-color image into a clear multispectral image signal weakening the image edge characteristic in a multispectral edge calculation terminal.
As a further improvement scheme of the technical scheme: the multispectral edge calculation terminal comprises a display screen, a host and a man-machine interaction device.
An imaging focusing method and a control method of a silicon-based multispectral signal further comprise a control method, and the control method specifically comprises the following procedures:
(1) firstly, a multispectral imaging camera faces to a shot object, then a coarse-definition multispectral image signal is obtained through a coarse focusing knob, the coarse-definition multispectral image signal is sent to a high-pass filter, and meanwhile a group of full-color images of the object are shot and transmitted to a multispectral edge computing terminal;
(2) reuse of
The function carries out convolution operation on the multispectral image signal, the multispectral image signal is input into a high-pass filter after being subjected to smooth filtering, the edge effect of the image can be improved after multiple times of high-pass filtering, and finally the image after high-pass filtering is transmitted to a multispectral edge computing terminal, wherein the function carries out convolution operation on the multispectral image signal, the multispectral image signal is input into the high-pass filter after being subjected to high-pass filtering, the edge effect of the image can be improved, and the image after high-pass filtering is transmitted to the multispectral edge computing terminal, and the multispectral image signal is obtained through high-pass filtering
The convolution operation process of the function is that firstly, an image for convolution operation is loaded and converted into a floating point type, then a convolution kernel function of the function is defined according to the requirement, then the convolution function is compiled, corresponding parameters are filled in, and finally, a target image is converted into an unsigned character type and the image after convolution is displayed;
(3) the method comprises the steps of controlling a multispectral edge computing terminal, injecting edge information of a full-color image into a clear multispectral image signal weakening image edge characteristics by using a self-adaptive edge extraction method so as to obtain a fused image with high-frequency detail information, and in the process of using the self-adaptive edge extraction method, in order to avoid that the detail information of the fused image is fuzzy when the injected edge information is less and the spectrum of the fused image is distorted when the injected edge information is excessive, the edge information amount injected into the multispectral image needs to be controlled by adopting the self-adaptive edge extraction method.
As a further improvement scheme of the technical scheme: the self-adaptive edge extraction method is characterized in that an image blocking and Otsu threshold value method is introduced on the basis of the self-adaptive edge extraction method, wherein the threshold value solving and edge connecting process comprises the following steps:
1) calculating the gradient mean value mu and the gradient variance delta of the whole image gradient matrix Gra (i, j), and if delta is larger than K, carrying out the next step, wherein K is a constant and represents the change degree of the target and background gradients in each region of the image;
2) averagely dividing Gra (i, j) into four blocks according to a quadtree [14] principle, respectively calculating the gradient mean value mu and the gradient variance delta of each subblock in the anticlockwise direction, if the gradient variance of the subblock accords with delta being larger than K, continuing to decompose according to the quadtree, and otherwise, jumping to the next step;
3) and determining a local threshold value of the segmentation, and applying an Otsu method to each gradient block which satisfies that delta is less than or equal to K to obtain an optimal segmentation threshold value and obtain a threshold value matrix with the same size as the sub-gradient matrix. Judging whether the quadtree decomposition is finished on all the subblocks, and returning to the previous step if the subblocks which are not decomposed exist;
4) through the above three steps, a threshold matrix TA having the same size as the Gra (i, j) is obtained. However, due to the complexity and the regionality of background information and the fact that the target is dispersed in different sub-images, a relatively obvious blocky effect appears in the segmentation of the whole image, in order to balance the blocky effect among the sub-images, a new threshold matrix can be obtained by using an interpolation method, so that the influence of the blocky effect can be obviously relieved, and TA is interpolated by performing equal-interval interpolation on four adjacent rows (four columns) of two adjacent sub-blocks to obtain a final threshold matrix TH which is used as a high threshold matrix, wherein a low threshold matrix TL is 0 and 4 multiplied by TH;
5) after obtaining the threshold segmentation matrices TH and TL, threshold segmentation and edge point connection need to be performed on the gradient matrix Gra (i, j).
Compared with the prior art, the invention has the beneficial effects that:
firstly, when shooting, a clear rough clear multispectral image signal with a small amount of noise is quickly obtained through coarse focusing, meanwhile, a group of full-color images of objects are shot, then the multispectral image signal is denoised through a high-pass filter to obtain a clear multispectral image signal with weakened image edge characteristics, finally, the edge information of the full-color image is injected into the clear multispectral image signal with weakened image edge characteristics to obtain a fused image with high-frequency detail information, and then the high-definition image can be obtained.
The foregoing description is only an overview of the technical solutions of the present invention, and in order to make the technical solutions of the present invention more clearly understood and to implement them in accordance with the contents of the description, the following detailed description is given with reference to the preferred embodiments of the present invention and the accompanying drawings. The detailed description of the present invention is given in detail by the following examples and the accompanying drawings.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
fig. 1 is a schematic diagram of an imaging focusing method and a control method for silicon-based multispectral signals according to the present invention;
fig. 2 is a schematic diagram of an imaging focusing method and a control method of a silicon-based multispectral signal according to the present invention.
Detailed Description
The principles and features of this invention are described below in conjunction with the following drawings, which are set forth by way of illustration only and are not intended to limit the scope of the invention. The invention is described in more detail in the following paragraphs by way of example with reference to the accompanying drawings. Advantages and features of the present invention will become apparent from the following description and from the claims. It is to be noted that the drawings are in a very simplified form and are not to precise scale, which is merely for the purpose of facilitating and distinctly claiming the embodiments of the present invention.
It will be understood that when an element is referred to as being "secured to" another element, it can be directly on the other element or intervening elements may also be present. When a component is referred to as being "connected" to another component, it can be directly connected to the other component or intervening components may also be present. When a component is referred to as being "disposed on" another component, it can be directly on the other component or intervening components may also be present. The terms "vertical," "horizontal," "left," "right," and the like as used herein are for illustrative purposes only.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
Referring to fig. 1-2, in an embodiment of the present invention, an imaging focusing method and a control method for silicon-based multispectral signals include the following steps:
firstly, during shooting, quickly obtaining a clear rough clear multispectral image signal with a small amount of noise through coarse focusing, and shooting a group of full-color images of an object;
secondly, enabling the multispectral image signals in the previous step to pass through a denoising module, wherein the denoising module can remove noise of the multispectral image signals, but can weaken the edge characteristics of the image;
and thirdly, injecting edge information of the full-color image into a clear multispectral image signal weakening image edge characteristics to obtain a fused image with high-frequency detail information, namely obtaining a high-definition image. But when the injected edge information is less, the detail information of the fused image is blurred, and when the injected edge information is too much, the spectrum of the fused image is distorted. And controlling the amount of edge information injected into the multispectral image by adopting an adaptive edge extraction method. On the basis of deep research on the traditional self-adaptive edge extraction method, an image blocking and Otsu threshold method is introduced, and corresponding improvement is made on threshold value solving and edge connection. And counting the number of points of each gradient value in the image gradient matrix N (i, j) after the non-maximum value is inhibited to form a gradient histogram. The gradient of the histogram was found to be concentrated in 0-50. The gradients are distributed in a narrow area and are not distributed in the whole image area, so that subsequent image segmentation is not facilitated, the gradient range can be expanded by adopting contrast expansion, and a logarithmic transformation method is adopted. The logarithmic transformation formula is as follows
Gra (i, j) ═ Alb (N (i, j) +1), where the value of a is determined by the maximum value in N (i, j) and the gradient order L of the new gradient image.
Specifically, in the first step, during coarse focusing, an optical lens is rotated to collect images of a target scene at regular intervals, and when the position with the maximum definition is the position of the signal of the multispectral image with coarse focusing definition, the signal of the multispectral image with coarse focusing definition is obtained.
Specifically, in the second step, the denoising module is a high-pass filter.
Specifically, in the third step, when the injected edge information is less, the detail information of the fused image is blurred, and when the injected edge information is excessive, the spectrum distortion of the fused image is caused, and the edge information amount of the injected multispectral image signal is controlled by adopting a self-adaptive edge extraction method.
Specifically, in the first step, a multispectral imaging camera is used to photograph the object.
Specifically, in the third step, the edge information of the panchromatic image is injected into a clear multispectral image signal weakening the image edge characteristics in the multispectral edge calculation terminal.
Specifically, the multispectral edge calculation terminal comprises a display screen, a host and a man-machine interaction device.
An imaging focusing method and a control method of silicon-based multispectral signals comprise the following procedures:
(1) firstly, a multispectral imaging camera faces to a shot object, then a coarse-definition multispectral image signal is obtained through a coarse focusing knob, the coarse-definition multispectral image signal is sent to a high-pass filter, and meanwhile a group of full-color images of the object are shot and transmitted to a multispectral edge computing terminal;
(2) reuse of
The function carries out convolution operation on the multispectral image signal, the multispectral image signal is input into a high-pass filter after being subjected to smooth filtering, the edge effect of the image can be improved after multiple times of high-pass filtering, and finally the image after high-pass filtering is transmitted to a multispectral edge computing terminal, wherein the function carries out convolution operation on the multispectral image signal, the multispectral image signal is input into the high-pass filter after being subjected to high-pass filtering, the edge effect of the image can be improved, and the image after high-pass filtering is transmitted to the multispectral edge computing terminal, and the multispectral image signal is obtained through high-pass filtering
The convolution operation process of the function is that firstly, an image for convolution operation is loaded and converted into a floating point type, then a convolution kernel function of the function is defined according to the requirement, then the convolution function is compiled, corresponding parameters are filled in, and finally, a target image is converted into an unsigned character type and the image after convolution is displayed;
(3) controlling a multispectral edge calculation terminal, injecting edge information of a panchromatic image into a clear multispectral image signal weakening image edge characteristics by using a self-adaptive edge extraction method so as to obtain a fused image with high-frequency detail information, wherein in the process of using the self-adaptive edge extraction method, in order to avoid that the detail information of the fused image is fuzzy when the injected edge information is less, and the spectrum distortion of the fused image is caused when the injected edge information is excessive, the edge information amount injected into the multispectral image is controlled by adopting the self-adaptive edge extraction method, the self-adaptive edge extraction method is to introduce an image block and an Otsu threshold value method on the basis of a candy operator, wherein the threshold value obtaining and edge connecting process is as follows:
1) calculating the gradient mean value mu and the gradient variance delta of the whole image gradient matrix Gra (i, j), and if delta is larger than K, carrying out the next step, wherein K is a constant and represents the change degree of the target and background gradients in each region of the image;
2) averagely dividing Gra (i, j) into four blocks according to a quadtree [14] principle, respectively calculating the gradient mean value mu and the gradient variance delta of each subblock in the anticlockwise direction, if the gradient variance of the subblock accords with delta being larger than K, continuing to decompose according to the quadtree, and otherwise, jumping to the next step;
3) and determining a local threshold value of the segmentation, and applying an Otsu method to each gradient block which satisfies that delta is less than or equal to K to obtain an optimal segmentation threshold value and obtain a threshold value matrix with the same size as the sub-gradient matrix. Judging whether the quadtree decomposition is finished on all the subblocks, and returning to the previous step if the subblocks which are not decomposed exist;
4) through the above three steps, a threshold matrix TA having the same size as the Gra (i, j) is obtained. However, due to the complexity and the regionality of background information and the fact that the target is dispersed in different sub-images, a relatively obvious blocky effect appears in the segmentation of the whole image, in order to balance the blocky effect among the sub-images, a new threshold matrix can be obtained by using an interpolation method, so that the influence of the blocky effect can be obviously relieved, and TA is interpolated by performing equal-interval interpolation on four adjacent rows (four columns) of two adjacent sub-blocks to obtain a final threshold matrix TH which is used as a high threshold matrix, wherein a low threshold matrix TL is 0 and 4 multiplied by TH;
5) after obtaining the threshold segmentation matrices TH and TL, threshold segmentation and edge point connection need to be performed on the gradient matrix Gra (i, j).
The working principle of the invention is as follows:
firstly, during shooting, a clear rough clear multispectral image signal with a small amount of noise is quickly obtained through coarse focusing, meanwhile, a group of full-color images of objects are shot, then the multispectral image signal is denoised through a high-pass filter to obtain a clear multispectral image signal with weakened image edge characteristics, and finally, edge information of the full-color image is injected into the clear multispectral image signal with the weakened image edge characteristics to obtain a fused image with high-frequency detail information, and the high-definition image can be obtained.
The foregoing is merely a preferred embodiment of the invention and is not intended to limit the invention in any manner; those skilled in the art can readily practice the invention as shown and described in the drawings and detailed description herein; however, those skilled in the art should appreciate that they can readily use the disclosed conception and specific embodiments as a basis for designing or modifying other structures for carrying out the same purposes of the present invention without departing from the scope of the invention as defined by the appended claims; meanwhile, any changes, modifications, and evolutions of the equivalent changes of the above embodiments according to the actual techniques of the present invention are still within the protection scope of the technical solution of the present invention.
Claims (9)
1. An imaging focusing method and a control method of a silicon-based multispectral signal are characterized by comprising the following steps:
firstly, during shooting, quickly obtaining a clear rough clear multispectral image signal with a small amount of noise through coarse focusing, and shooting a group of full-color images of an object;
secondly, enabling the multispectral image signals in the previous step to pass through a denoising module, wherein the denoising module can remove noise of the multispectral image signals, but can weaken the edge characteristics of the image;
and thirdly, injecting the edge information of the full-color image into a clear multispectral image signal weakening the edge characteristics of the image so as to obtain a fused image with high-frequency detail information, and thus obtaining a high-definition image.
2. The method according to claim 1, wherein in the first step, during coarse focusing, the optical lens is rotated to collect images of the target scene at regular intervals, and when the position with the maximum resolution is the position of the multispectral image signal with coarse focusing resolution, the method is characterized in that the position with coarse focusing resolution is obtained.
3. The method for focusing and controlling imaging of silicon-based multispectral signals as recited in claim 1, wherein the denoising module in the second step is a high-pass filter.
4. The method for focusing and controlling imaging of silicon-based multispectral signals according to claim 1, wherein in the third step, the amount of edge information injected into the multispectral image signals is controlled by using an adaptive edge extraction method, wherein the amount of injected edge information is less than the amount of injected edge information, which results in blurring of detail information of the fused image and the amount of injected edge information which results in spectral distortion of the fused image.
5. The method for focusing and controlling the imaging of silicon-based multispectral signals as recited in claim 1, wherein in the first step, the object is photographed using a multispectral imaging camera.
6. The method for focusing and controlling imaging of silicon-based multispectral signals according to claim 1, wherein in the third step, edge information of the panchromatic image is injected into the sharp multispectral image signal weakening the edge characteristics of the image in the multispectral edge calculation terminal.
7. The method for focusing and controlling imaging of silicon-based multispectral signals as claimed in claim 6, wherein the multispectral edge computing terminal comprises a display screen, a host and a human-computer interaction device.
8. The imaging focusing method and the control method of the silicon-based multispectral signal are characterized by further comprising a control method, wherein the control method specifically comprises the following procedures:
(1) firstly, a multispectral imaging camera faces to a shot object, then a coarse-definition multispectral image signal is obtained through a coarse focusing knob, the coarse-definition multispectral image signal is sent to a high-pass filter, and meanwhile a group of full-color images of the object are shot and transmitted to a multispectral edge computing terminal;
(2) reuse of
The function carries out convolution operation on the multispectral image signal, the multispectral image signal is input into a high-pass filter after being subjected to smooth filtering, and the multispectral image signal is subjected to multiple high-pass filteringFurther improving the edge effect of the image, and finally transmitting the high-pass filtered image to a multispectral edge computing terminal, wherein
The convolution operation process of the function is that firstly, an image for convolution operation is loaded and converted into a floating point type, then a convolution kernel function of the function is defined according to the requirement, then the convolution function is compiled, corresponding parameters are filled in, and finally, a target image is converted into an unsigned character type and the image after convolution is displayed;
(3) the method comprises the steps of controlling a multispectral edge computing terminal, injecting edge information of a full-color image into a clear multispectral image signal weakening image edge characteristics by using a self-adaptive edge extraction method so as to obtain a fused image with high-frequency detail information, and in the process of using the self-adaptive edge extraction method, in order to avoid that the detail information of the fused image is fuzzy when the injected edge information is less and the spectrum of the fused image is distorted when the injected edge information is excessive, the edge information amount injected into the multispectral image needs to be controlled by adopting the self-adaptive edge extraction method.
9. The method for focusing and controlling imaging of silicon-based multispectral signals according to claim 8, wherein the adaptive edge extraction method is based on the adaptive edge extraction method, and comprises the following steps of introducing image blocking and Otsu threshold method, wherein the threshold value calculation and edge connection process comprises:
1) calculating the gradient mean value mu and the gradient variance delta of the whole image gradient matrix Gra (i, j), and if delta is larger than K, carrying out the next step, wherein K is a constant and represents the change degree of the target and background gradients in each region of the image;
2) averagely dividing Gra (i, j) into four blocks according to a quadtree [14] principle, respectively calculating the gradient mean value mu and the gradient variance delta of each subblock in the anticlockwise direction, if the gradient variance of the subblock accords with delta being larger than K, continuing to decompose according to the quadtree, and otherwise, jumping to the next step;
3) determining a local threshold value of segmentation, applying an Otsu method to each gradient block which satisfies that delta is less than or equal to K to obtain an optimal segmentation threshold value, obtaining a threshold matrix with the same size as the sub-gradient matrix, judging whether the quadtree decomposition is finished on all sub-blocks, and returning to the previous step if the sub-blocks which are not decomposed exist;
4) obtaining a threshold matrix TA with the same size as Gra (i, j) through the three steps, but because of the complexity and the regionality of background information and the fact that a target is dispersed in different sub-images, a more obvious blocky effect can appear in the segmentation of the whole image, in order to balance the blocky effect among the sub-images, a new threshold matrix can be obtained by using an interpolation method, so that the influence of the blocky effect can be obviously reduced, and the TA is interpolated, namely, performing equidistant interpolation on four adjacent rows (four columns) of two adjacent sub-blocks to obtain a final threshold matrix TH which is used as a high threshold matrix, wherein the low threshold matrix TL is 0 and 4 multiplied by TH;
5) after obtaining the threshold segmentation matrices TH and TL, threshold segmentation and edge point connection need to be performed on the gradient matrix Gra (i, j).
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