CN113395415B - Camera data processing method and system based on noise reduction technology - Google Patents
Camera data processing method and system based on noise reduction technology Download PDFInfo
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Abstract
The invention discloses a camera data processing method and system based on a noise reduction technology, and aims to solve the technical problems that image noise cannot be processed in the prior art, the noise can influence the whole process of image processing, acquisition and output, the definition of a picture is reduced, the quality of the picture is low, data is not stored in a blocking mode, and the utilization rate of a storage space is reduced. The camera data processing method and system based on the noise reduction technology comprises the following steps: extracting signals, converting the signals into digital images, denoising, image segmentation, enhancing the usefulness of the images, extracting moving objects, partitioning data and storing the partitioned data. The camera data processing method and system based on the noise reduction technology can perform complex nonlinear processing, solve the problem that the image quality of an actual image is reduced due to various parasitic effects, and effectively improve the image quality through the data processing module, so that the definition and quality of the image are improved, and the utilization rate of a storage space is improved.
Description
Technical Field
The invention belongs to the technical field of camera data processing, and particularly relates to a camera data processing method and system based on a noise reduction technology.
Background
The camera integrates components of image information conversion, storage, transmission and the like, has a digital access mode, is convenient to interact with a computer for processing, and can be widely used in a plurality of fields due to the continuous development of an electronic imaging technology at any time.
At present, the invention patent with patent number CN112437224A discloses a camera data processing method, which includes: capturing current frame camera image data called back by a camera, wherein the current frame camera image data is a frame of image data currently acquired by the camera; decoding a specified data source, and acquiring one frame of specified image data in the specified data source; replacing the current frame image data with the one frame designated image data; and performing operation corresponding to the current frame image data by using the specified image data.
In yet another aspect, the present application provides a camera data processing system, comprising: the data acquisition module is used for acquiring current frame camera image data called back by a camera, wherein the current frame camera image data is a frame of image data currently acquired by the camera; the data source decoding module is used for decoding a specified data source and acquiring one frame of specified image data in the specified data source; the data replacement module is used for replacing the current frame image data with the frame designated image data; and the data processing module is used for carrying out operation corresponding to the current frame image data by utilizing the specified image data. In yet another aspect, the present application further provides a computer device, including: a processor and a memory; wherein the processor is configured to execute a program stored in the memory; the memory is to store a program to at least: capturing current frame camera image data called back by a camera, wherein the current frame camera image data is a frame of image data currently acquired by the camera; decoding a specified data source, and acquiring one frame of specified image data in the specified data source; replacing the current frame image data with the one frame designated image data; and performing operation corresponding to the current frame image data by using the specified image data. The data processing method does not make any requirement on data actually acquired by a camera, avoids dependence on an actual scene when image data is acquired, cannot process image noise, the noise can influence the whole process of image processing, acquisition and output, the definition of a picture is reduced, the quality of the picture is low, the data is not stored in a blocking mode, and the utilization rate of a storage space is reduced.
Therefore, it is necessary to solve the problem that the camera data does not have the noise reduction function, so as to improve the use scene of the camera.
Disclosure of Invention
(1) Technical problem to be solved
The invention aims to provide a camera data processing method and system based on a noise reduction technology, aiming at solving the technical problems that image noise cannot be processed in the prior art, the noise can influence the whole process of image processing, acquisition and output, the definition of a picture is reduced, the quality of the picture is low, data is not stored in a blocking mode, and the utilization rate of a storage space is reduced.
(2) Technical scheme
(3) In order to solve the above technical problem, the present invention provides a camera data processing method based on noise reduction technology, comprising the following steps:
the method comprises the following steps: extracting corresponding initial image data signals, wherein the initial images are analog images, the signal comparison unit compares the analog signals with different reference voltages Ui for a plurality of times to enable the converted digital quantity to gradually approach to the corresponding value of the analog signals in numerical value, firstly, the highest bit of the register unit is set to be 1 by the time sequence control unit to enable the output digit to be 100 … … 0, the output digit is converted into the corresponding analog voltage U0 to be compared with the Ui, and if the U0 is more than the Ui, the highest bit 1 is cleared; if U0 < Ui, keeping the highest 1, recording as I, counting by the counting unit, repeating I = I +1, until the lowest bit, the state of the counting unit is the required digital quantity output, and obtaining the digital image;
step two: the converted digital image is sent to a data processing module to carry out denoising processing on the digital image, and based on a guide filtering algorithm,
first assume that the output of the guided filter function and the input satisfy a linear relationship within a two-dimensional window, as follows: q. q.sz=akCz+bk,
z
k,qz=pz-nzWhere q is the value of the output pixel, i.e. pdeorImages after noise or texture removal, nzRepresenting noise, C is the value of the input image, z and k are pixel indices, a and b are coefficients of a linear function when the window center is located at k, when the guide map is the input image, the guide filtering becomes a filtering operation that keeps edges, i.e., C = p, and taking the gradient on both sides of the upper representation can result in q '= aC', i.e., when the input map C has a gradient, the output q also has a gradient, μkAnd
indicates that C is in the local window wkIs the mean and variance, | ω | is the number of all pixels in the window, pk denotes p in the window wkϵ is the regularization parameter, q is the mean of (C = p)z=pzN can be simplified to ak=
，bk=(1-ak) μkIf ϵ =0, it is obvious that a =1 and b =0 is the solution with E (a, b) as the minimum value, the filter has no effect at this time, and the input is the output which is not fixed; if ϵ>0, i.e. image C in window wkIs kept fixed at this time
<<ϵ, then has ak0 and bk≈μkI.e. a weighted mean filtering is performed, and in the high-variance region, i.e. the image C is represented in the window wkThe change is relatively large, in this case
>>ϵ, then has ak1 and bkThe value is approximately equal to 0, the filtering effect on the image is very weak, the edge is kept, under the condition that the window size is not changed, the filtering effect is more obvious along with the increase of ϵ, and the output value q of a certain point is required to be specificzWhen in use, all the linear function values containing the point are averaged,
wherein the output value q is related to two mean values, i.e. the mean values of a and b in the window w, and two images a are obtained in the previous stepkAnd bkBoth perform box filtering to obtain two new graphs: az 'and bz', then multiplying the az 'by the guide image Cz, and adding bz' to obtain an output image q after final filtering;
step three: the digital image is divided into a plurality of mutually disjoint areas by carrying out graph segmentation operation on the digital image through a data processing module, so that all the areas have consistency, and the attribute characteristics between the adjacent areas have obvious difference;
step four: restoring the image to the original image under visual perception through an image data enhancement unit, enhancing the required information in the image and inhibiting other unnecessary information;
step five: detecting a change area through an image data target detection and motion detection unit, and extracting a motion target from a background image;
step six: when the Hash value of the sliding window is matched with a reference value, a sub-data block K1 is created, so that the size of the data block can reach an expected distribution, two integers E and P are predefined, if the Hash value of the data in the fixed window is f at a position K, if f mod E = P, the position is a boundary of the data block, the process is repeated until the whole file is divided into blocks to obtain sub-data blocks K2 and K3 … … Kn, each divided block calculates the fingerprint value of the block by using a Hash function and compares the fingerprint value with the stored sub-data block, if the same fingerprint value is detected, the sub-data block represented by the divided block is deleted, and if the same fingerprint value is detected, a new sub-data block is stored;
step seven: comparing the sub-data block K1 in the block unit with the size of the residual space in S1, storing K1 in S1 when K1 is smaller than the residual space in S1, continuing to compare K1 with the size of the residual space in S2 when K1 is larger than the residual space in S1, storing K1 in S2 when K1 is smaller than the residual space in S2, continuing to compare K1 with the size of the residual space in S3 when K1 is larger than the residual space in S2 until the storage of K1 is completed, and storing K2 and K3 … … Kn in S1, S2 and S3 … … Sn in turn.
A camera data processing system based on noise reduction technology, comprising the camera data processing method based on noise reduction technology of claim 1, the system comprising a data conversion module, a data processing module and a data storage module, the data conversion module comprises a ramp signal generation unit, a signal comparison unit, a counting unit, a timing control unit and a registering unit, the ramp signal generation unit converts the level of a sampling signal into a ramp signal of time axis length information, the signal comparison unit compares the ramp with the sampling signal, the counting unit generates global title, the global title counts up as the ramp signal decreases, the timing control unit is used for controlling the time points of ramp signal generation and global title generation, the data processing module comprises an image data denoising unit, an image segmentation unit, an image data enhancement unit and an image data target detection and motion detection unit, the image data denoising unit is used for solving the problem that the image quality of an actual image is reduced due to various parasitic effects, the image segmentation unit divides the image into a plurality of mutually-disjoint areas, the areas are made to have consistency, the attribute characteristics of adjacent areas are obviously different, the image data enhancement unit can improve the blurring condition of the image and emphasize and enhance the locality of the image, the image data target detection and motion detection unit is used for identifying points with obvious brightness change in the digital image, the data storage module comprises a blocking unit and a storage unit, the blocking unit is used for blocking the image data, and the storage unit stores the data blocks blocked by the blocking unit.
Preferably, the detection method preset in the image data target detection and motion detection unit is a background subtraction technique, which detects a motion region by using a difference between a current image and a background image, and the image data enhancement unit includes spatial processing and frequency domain processing.
Preferably, the reference voltage Ui is preset in the signal comparison unit.
Preferably, the image data denoising unit obtains a new central pixel value by analyzing direct relation between a central pixel and other adjacent pixels in a gray scale space within a window of a certain size, and a guided filtering algorithm is preset in the image data denoising unit.
Preferably, a blocking rule is preset in the blocking unit, the file to be stored is divided into data blocks with variable lengths, the length of the data block is between a specified minimum value and a specified maximum value, and the data blocks with variable lengths are divided by using a sliding window.
Preferably, the memory unit is divided into a plurality of memory spaces S1, S2, S3 … … Sn.
(3) Advantageous effects
Compared with the prior art, the invention has the beneficial effects that: the camera data processing method and system based on the noise reduction technology convert the analog image into the digital image by using the data conversion module, have high processing precision and rich processing content, can perform complex nonlinear processing, have flexible flexibility and are convenient for processing the image, the noise reduction processing is performed on the digital image by using the data processing module, the problem of image quality reduction of the actual image due to various parasitic effects is solved, the quality of the image can be effectively improved by using the data processing module, the signal-to-noise ratio is increased, the information carried by the original image is better embodied, the image file to be processed is improved, the definition and the quality degree of the image are improved, the processed image file is blocked and stored by using the data storage module, the size of the blocked image file is variable, and the utilization rate of a storage space is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the technical solutions in the prior art will be briefly described below, it is obvious that the drawings in the following description are only one embodiment of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a schematic view of a workflow of a camera data processing method based on a noise reduction technology according to the present invention;
fig. 2 is a schematic diagram of a frame structure of a camera data processing system based on a noise reduction technology according to the present invention.
Detailed Description
In order to make the technical means, the original characteristics, the achieved purposes and the effects of the invention easily understood and obvious, the technical solutions in the embodiments of the present invention are clearly and completely described below to further illustrate the invention, and obviously, the described embodiments are only a part of the embodiments of the present invention, but not all the embodiments.
Example 1
The present embodiment is a camera data processing method and system based on noise reduction technology, the work flow diagram of the method is shown in fig. 1, and the steps are as follows:
the method comprises the following steps: extracting corresponding initial image data signals, wherein the initial images are analog images, the signal comparison unit compares the analog signals with different reference voltages Ui for a plurality of times to enable the converted digital quantity to gradually approach to the corresponding value of the analog signals in numerical value, firstly, the highest bit of the register unit is set to be 1 by the time sequence control unit to enable the output digit to be 100 … … 0, the output digit is converted into the corresponding analog voltage U0 to be compared with the Ui, and if the U0 is more than the Ui, the highest bit 1 is cleared; if U0 < Ui, keeping the highest 1, recording as I, counting by the counting unit, repeating I = I +1, until the lowest bit, the state of the counting unit is the required digital quantity output, and obtaining the digital image;
step two: the converted digital image is sent to a data processing module to carry out denoising processing on the digital image, and based on a guide filtering algorithm,
first assume that the output of the guided filter function and the input satisfy a linear relationship within a two-dimensional window, as follows: q. q.sz=akCz+bk,
z
k,qz=pz-nzWhere q is the value of the output pixel, i.e. p the image after noise or texture removal, nzRepresenting noise, C is the value of the input image, z and k are pixel indices, a and b are coefficients of a linear function when the window center is located at k, when the guide map is the input image, the guide filtering becomes a filtering operation that keeps edges, i.e., C = p, and taking the gradient on both sides of the upper representation can result in q '= aC', i.e., when the input map C has a gradient, the output q also has a gradient, μkAnd
indicates that C is in the local window wkIs the mean and variance, | ω | is the number of all pixels in the window, pk denotes p in the window wkϵ is the regularization parameter, q is the mean of (C = p)z=pzN can be simplified to ak=
，bk=(1-ak) μkIf ϵ =0, it is obvious that a =1 and b =0 is the solution with E (a, b) as the minimum value, the filter has no effect at this time, and the input is the output which is not fixed; if ϵ>0, i.e. image C in window wkIs kept fixed at this time
<<ϵ, then has ak0 and bk≈μkI.e. a weighted mean filtering is performed, and in the high-variance region, i.e. the image C is represented in the window wkThe change is relatively large, in this case
>>ϵ, then has ak1 and bkThe value is approximately equal to 0, the filtering effect on the image is very weak, the edge is kept, under the condition that the window size is not changed, the filtering effect is more obvious along with the increase of ϵ, and the output value q of a certain point is required to be specificzWhen in use, all the linear function values containing the point are averaged,
wherein the output value q is related to two mean values, i.e. the mean values of a and b in the window w, and two images a are obtained in the previous stepkAnd bkBoth perform box filtering to obtain two new graphs: az 'and bz', then multiplying the az 'by the guide image Cz, and adding bz' to obtain an output image q after final filtering;
step three: the digital image is divided into a plurality of mutually disjoint areas by carrying out graph segmentation operation on the digital image through a data processing module, so that all the areas have consistency, and the attribute characteristics between the adjacent areas have obvious difference;
step four: restoring the image to the original image under visual perception through an image data enhancement unit, enhancing the required information in the image and inhibiting other unnecessary information;
step five: detecting a change area through an image data target detection and motion detection unit, and extracting a motion target from a background image;
step six: when the Hash value of the sliding window is matched with a reference value, a sub-data block K1 is created, so that the size of the data block can reach an expected distribution, two integers E and P are predefined, if the Hash value of the data in the fixed window is f at a position K, if f mod E = P, the position is a boundary of the data block, the process is repeated until the whole file is divided into blocks to obtain sub-data blocks K2 and K3 … … Kn, each divided block calculates the fingerprint value of the block by using a Hash function and compares the fingerprint value with the stored sub-data block, if the same fingerprint value is detected, the sub-data block represented by the divided block is deleted, and if the same fingerprint value is detected, a new sub-data block is stored;
step seven: comparing the sub-data block K1 in the block unit with the size of the residual space in S1, storing K1 in S1 when K1 is smaller than the residual space in S1, continuing to compare K1 with the size of the residual space in S2 when K1 is larger than the residual space in S1, storing K1 in S2 when K1 is smaller than the residual space in S2, continuing to compare K1 with the size of the residual space in S3 when K1 is larger than the residual space in S2 until the storage of K1 is completed, and storing K2 and K3 … … Kn in S1, S2 and S3 … … Sn in turn.
A camera data processing system based on noise reduction technology comprises the camera data processing method based on noise reduction technology, and the system comprises a data conversion module, a data processing module and a data storage module, wherein the data conversion module comprises a ramp signal generation unit, a signal comparison unit, a counting unit, a time sequence control unit and a registering unit, the ramp signal generation unit converts the level of a sampling signal into a ramp signal of time axis length information, the signal comparison unit compares the ramp with the sampling signal, the counting unit generates global title codes, the global title codes are counted in an increasing mode along with the reduction of the ramp signal, the time sequence control unit is used for controlling the time points of generating the ramp signal and generating the global title codes, the data processing module comprises an image data denoising unit, an image segmentation unit, an image data enhancement unit and an image data target detection and motion detection unit, the image data denoising unit is used for solving the problem that the image quality of an actual image is reduced due to various parasitic effects, the image segmentation unit divides the image into a plurality of mutually disjoint areas, each area has consistency, the attribute characteristics of adjacent areas have obvious difference, the image data enhancement unit can improve the blurring condition of the image and emphasize and enhance the locality of the image, the image data target detection and motion detection unit is used for identifying points with obvious brightness change in the digital image, the data storage module comprises a blocking unit and a storage unit, the blocking unit is used for blocking the image data, and the storage unit stores the data blocks blocked by the blocking unit.
The image data target detection and motion detection unit is used for detecting a motion area by utilizing a difference meter between a current image and a background image, and the image data enhancement unit comprises space domain processing and frequency domain processing.
Meanwhile, a reference voltage Ui is preset in the signal comparison unit, the image data denoising unit obtains a new central pixel value by analyzing the direct relation between the central pixel and other adjacent pixels in a gray scale space in a window with a certain size, and a guide filtering algorithm is preset in the image data denoising unit.
TABLE 1
In addition, a blocking rule is preset in the blocking unit, the file to be stored is divided into data blocks with variable lengths, the length of each data block is between a specified minimum value and a specified maximum value, the data blocks with variable lengths are divided by a sliding window, and a plurality of storage spaces S1, S2 and S3 … … Sn are divided in the storage unit.
A schematic diagram of a system framework structure of the camera data processing method and system based on the noise reduction technology is shown in fig. 2.
Having thus described the principal technical features and basic principles of the invention, and the advantages associated therewith, it will be apparent to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, but is capable of other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.
Furthermore, it should be understood that although the present description is described in terms of various embodiments, not every embodiment includes only a single embodiment, and such descriptions are provided for clarity only, and those skilled in the art will recognize that the embodiments described herein can be combined as a whole to form other embodiments as would be understood by those skilled in the art.
Claims (7)
1. The camera data processing method based on the noise reduction technology is characterized by comprising the following steps: the method comprises the following steps:
the method comprises the following steps: extracting corresponding initial image data signals, wherein the initial images are analog images, the signal comparison unit compares the analog signals with different reference voltages Ui for a plurality of times to enable the converted digital quantity to gradually approach to the corresponding value of the analog signals in numerical value, firstly, the highest bit of the register unit is set to be 1 by the time sequence control unit to enable the output digit to be 100 … … 0, the output digit is converted into the corresponding analog voltage U0 to be compared with the Ui, and if the U0 is more than the Ui, the highest bit 1 is cleared; if U0 < Ui, keeping the highest 1, recording as I, counting by the counting unit, repeating I = I +1, until the lowest bit, the state of the counting unit is the required digital quantity output, and obtaining the digital image;
step two: the converted digital image is sent to a data processing module to carry out denoising processing on the digital image, and based on a guide filtering algorithm,
first assume that the output of the guided filter function and the input satisfy a linear relationship within a two-dimensional window, as follows: q. q.sz=akCz+bk,
z
k,qz=pz-nzWhere q is the value of the output pixel, i.e. p the image after noise or texture removal, nzRepresenting noise, C is the value of the input image, z and k are pixel indices, a and b are coefficients of a linear function when the window center is located at k, when the guide map is the input image, the guide filtering becomes a filtering operation that keeps edges, i.e., C = p, and taking the gradient on both sides of the upper representation can result in q '= aC', i.e., when the input map C has a gradient, the output q also has a gradient, μkAnd
indicates that C is in the local window wkIs the mean and variance, | ω | is the number of all pixels in the window, pk denotes p in the window wkϵ is the regularization parameter, q is the mean of (C = p)z=pzN can be simplified to ak=
，bk=(1-ak) μkIf ϵ =0, it is obvious that a =1 and b =0 is the solution with E (a, b) as the minimum value, the filter has no effect at this time, and the input is the output which is not fixed; if ϵ>0, i.e. image C in window wkIs kept fixed at this time
<<ϵ, then has ak0 and bk≈μkI.e. a weighted mean filtering is performed, and in the high-variance region, i.e. the image C is represented in the window wkThe change is relatively large, in this case
>>ϵ, then has ak1 and bkThe value is approximately equal to 0, the filtering effect on the image is very weak, the edge is kept, under the condition that the window size is not changed, the filtering effect is more obvious along with the increase of ϵ, and the output value q of a certain point is required to be specificzWhen in use, all the linear function values containing the point are averaged,
wherein the output value q is related to two mean values, i.e. the mean values of a and b in the window w, and two images a are obtained in the previous stepkAnd bkBoth perform box filtering to obtain two new graphs: az 'and bz', then multiplying the az 'by the guide image Cz, and adding bz' to obtain an output image q after final filtering;
step three: the digital image is divided into a plurality of mutually disjoint areas by carrying out graph segmentation operation on the digital image through a data processing module, so that all the areas have consistency, and the attribute characteristics between the adjacent areas have obvious difference;
step four: restoring the image to the original image under visual perception through an image data enhancement unit, enhancing the required information in the image and inhibiting other unnecessary information;
step five: detecting a change area through an image data target detection and motion detection unit, and extracting a motion target from a background image;
step six: when the Hash value of the sliding window is matched with a reference value, a sub-data block K1 is created, so that the size of the data block can reach an expected distribution, two integers E and P are predefined, if the Hash value of the data in the fixed window is f at a position K, if f mod E = P, the position is a boundary of the data block, the process is repeated until the whole file is divided into blocks to obtain sub-data blocks K2 and K3 … … Kn, each divided block calculates the fingerprint value of the block by using a Hash function and compares the fingerprint value with the stored sub-data block, if the same fingerprint value is detected, the sub-data block represented by the divided block is deleted, and if the same fingerprint value is detected, a new sub-data block is stored;
step seven: comparing the sub-data block K1 in the block unit with the size of the residual space in S1, storing K1 in S1 when K1 is smaller than the residual space in S1, continuing to compare K1 with the size of the residual space in S2 when K1 is larger than the residual space in S1, storing K1 in S2 when K1 is smaller than the residual space in S2, continuing to compare K1 with the size of the residual space in S3 when K1 is larger than the residual space in S2 until the storage of K1 is completed, and storing K2 and K3 … … Kn in S1, S2 and S3 … … Sn in turn.
2. A camera data processing system based on noise reduction technology, characterized in that it comprises the camera data processing method based on noise reduction technology as claimed in claim 1, the system comprises a data conversion module, a data processing module and a data storage module, the data conversion module comprises a ramp signal generation unit, a signal comparison unit, a counting unit, a timing control unit and a registering unit, the ramp signal generation unit converts the level of the sampling signal into a ramp signal of time axis length information, the signal comparison unit compares the ramp with the sampling signal, the counting unit generates global title code, the global title code counts up with the decrease of the ramp signal, the timing control unit is used to control the time point of ramp signal generation and global title code generation, the data processing module comprises an image data denoising unit, an image segmentation unit, a frame buffer unit, and a frame buffer unit, The image data enhancement unit can improve the blurring condition of the image and emphasize and enhance the locality of the image, the image data target detection and motion detection unit is used for identifying points with obvious brightness change in the digital image, the data storage module comprises a blocking unit and a storage unit, the blocking unit is used for blocking the image data, and the storage unit stores the data blocks blocked by the blocking unit.
3. The system of claim 2, wherein the detection method preset in the image data object detection and motion detection unit is a background subtraction technique, which uses a difference between a current image and a background image to detect a motion region, and the image data enhancement unit comprises spatial processing and frequency domain processing.
4. The camera data processing system based on noise reduction technology of claim 2, wherein the reference voltage Ui is preset in the signal comparison unit.
5. The noise reduction technique-based camera data processing system according to claim 2, wherein the image data denoising unit obtains a new central pixel value by analyzing direct relations between the central pixel and other adjacent pixels in a gray space within a window of a certain size, and a guiding filtering algorithm is preset in the image data denoising unit.
6. The camera data processing system based on noise reduction technology as claimed in claim 2, wherein the blocking unit is preset with a blocking rule, the file to be stored is divided into variable-length data blocks, the length of the data blocks is between a specified minimum value and a specified maximum value, and the variable-length data blocks are divided by a sliding window.
7. The camera data processing system based on noise reduction technology as claimed in claim 2, wherein the storage unit is divided into a plurality of storage spaces S1, S2, S3 … … Sn.
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