CN113592741A - Digital image processing method - Google Patents
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- CN113592741A CN113592741A CN202110889526.XA CN202110889526A CN113592741A CN 113592741 A CN113592741 A CN 113592741A CN 202110889526 A CN202110889526 A CN 202110889526A CN 113592741 A CN113592741 A CN 113592741A
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Abstract
The invention discloses a digital image processing method, which comprises the following steps: s1: acquiring a digital image under ambient light interference, S2: obtaining an ambient light interference area image of the digital image according to the gray scale information of the digital image; s3: acquiring a characteristic subregion image in the ambient light interference region image; s4: processing the characteristic subregion image to obtain a processed characteristic subregion image; s5: carrying out noise reduction processing on the processed characteristic subregion image to obtain a new characteristic subregion image; s6: and carrying out normalization processing on the new characteristic subregion image and the reference subregion image to obtain a new digital image. The digital image processing method provided by the invention can solve the problem that the existing image processing method can not meet the requirement of image noise reduction.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to a digital image processing method.
Background
The continuous heat-force coupling deformation of the ablation layer composite material refers to that external load is applied to the ablation layer composite material when the ablation layer composite material is gradually heated to over 2000 ℃ from the room temperature condition, the mechanical properties and the damage mechanism of the ablation layer material in the heat-force coupling process are researched, and the method is a hot spot concerned by research of current mechanics, aviation, aerospace, materials and other scientific researches. When the space craft (missile, rocket, airship, etc.) rushes out of the atmosphere and returns to the ground (reentry), the ablation layer material itself can generate various endothermic physical and chemical changes such as decomposition, melting, evaporation, sublimation, etc. under the action of heat flow, and a large amount of heat is taken away by means of the self-mass consumption of the material, thereby preventing the heat from being transmitted to the internal structure of the material. In the fields of aerospace, national defense, military industry and the like, the ablation layer composite material is often positioned at the forefront end of aircrafts such as high supersonic aircrafts, ballistic missiles and the like in the flight process, so that high requirements on the ablation performance and high-temperature bearing performance of the ablation layer composite material are provided.
In the high-temperature dynamic deformation process, especially in the combustion process, the collection of the surface image of the measured object can be interfered due to large light intensity change.
When the deflection is too big, ambient light can arouse the optical noise at the measurement object surface, ambient optical noise often can make in the image certain or certain several regional sudden changes of luminance take place, because in the high temperature, the deflection is great, radiant intensity often can change along with the deformation process, the light distribution that leads to the measured object surface in the deformation process can take place violent the change, when the intensity of ambient light is less strong, digital image correlation operation can carry out certain elimination, but if the intensity of ambient light is higher, thereby when leading to measured object surface light intensity step oversize, image processing and analytic process are probably influenced. In order to be able to perform digital image correlation analysis under the condition of ambient light interference, the interference of ambient light, especially strong ambient light, must be eliminated. Generally speaking, no matter deformation detection is carried out in a laboratory environment or an outdoor environment, interference of the artificial light source on detection and measurement is relatively small, and manual intervention can be carried out, so that the fact that the reflection intensity of the artificial light source on the surface of a test piece is uniform and stable in the deformation process is guaranteed. During the deformation of the test piece, the main noise source is the sudden change of the radiation intensity, so the light noise must be suppressed.
The optical noise is mainly composed of the heating light source and the external field sunlight, and can be generally considered as noise interference caused by the directional rays of the heating light source without scattering and reflection. The noise reduction processing method for two-dimensional images can be basically divided into two categories, namely a spatial domain method and a frequency domain method. The former is to directly perform data calculation on an original image and process the gradation value of a pixel. It is divided into two categories: one is to perform point-by-point operation on the image, which is called point operation; another type is to perform operations on the spatial domain associated with the neighborhood of the processing pixel, called local operations. The frequency domain method is to process the image in the frequency domain, correspondingly process the transformed coefficient, and then perform inverse transformation to achieve the purpose of image noise reduction. The distribution of the ambient light interference is compared with normal data to form non-random superimposed data, and the processing in the spatial domain cannot meet the requirement of noise reduction.
Disclosure of Invention
The invention aims to provide a digital image processing method to solve the problem that the existing image processing method cannot meet the requirement of image noise reduction.
The technical scheme for solving the technical problems is as follows:
the invention provides a digital image processing method, which comprises the following steps:
s1: a digital image under ambient light interference is acquired,
s2: obtaining an ambient light interference area image of the digital image according to the gray scale information of the digital image;
s3: acquiring a characteristic subregion image in the ambient light interference region image;
s4: carrying out noise reduction processing on the characteristic subregion image to obtain a new characteristic subregion image;
s5: and carrying out normalization processing on the new characteristic subregion image and the reference subregion image to obtain a new digital image.
Optionally, in step S1, a digital image under the interference of the ambient light is acquired by using a camera.
Alternatively, in step S2, the ambient light interference region is determined according to the jump of the gray scale information, the light intensity of the ambient light is determined according to the size of the jump, and the position and size of the image of the ambient light interference region are determined according to the light intensity and the ambient light interference region.
Alternatively, the step S3 includes the following substeps:
s31: determining the area size and the gray information of the characteristic subarea;
s32: and according to the area size, performing two-dimensional discrete Fourier transform on the gray information of the characteristic subarea to obtain a transformed characteristic subarea image.
Optionally, in step S32, the performing two-dimensional discrete fourier transform on the gray scale information includes:
in the formula, F (u, v) represents a fourier transform function of the characteristic subregion image, and F (x, y) represents gradation information within the characteristic subregion image; m, N is constant and represents the region size of the characteristic subregion image; i represents an imaginary number mark in Fourier transform, x and y represent area image coordinates of the characteristic sub-area, and u and v represent Fourier space coordinates after Fourier transform is carried out on the characteristic sub-area.
Alternatively, in step S33, the transformed feature subregion image is subjected to noise reduction processing by using a low-pass filter.
Optionally, the low pass filter is a stationary wavelet filter.
Optionally, in step S1, the ambient light is dynamic ambient light.
The invention has the following beneficial effects:
according to the technical scheme, namely the digital image processing method provided by the invention can improve the accuracy and stability of image processing under the influence of light noise for filtering and noise reduction of the digital image under the interference of ambient light, thereby further expanding the application scene of digital image acquisition and bringing new challenges to the field of image processing.
Drawings
FIG. 1 is a flow chart of a digital image processing method provided by the present invention;
FIG. 2 is a flowchart illustrating the substeps of step S3 in FIG. 1;
fig. 3 is a spectrum energy diagram of the characteristic subregion image and the reference subregion image of the digital image processing method provided by 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.
Examples
The technical scheme for solving the technical problems is as follows:
the present invention provides a digital image processing method, as shown with reference to fig. 1, the digital image processing method including:
s1: a digital image under ambient light interference is acquired,
s2: obtaining an ambient light interference area image of the digital image according to the gray scale information of the digital image;
s3: acquiring a characteristic subregion image in the ambient light interference region image;
s4: carrying out noise reduction processing on the characteristic subregion image to obtain a new characteristic subregion image;
s5: and carrying out normalization processing on the new characteristic subregion image and the reference subregion image to obtain a new digital image.
Therefore, the new digital image obtained by the digital image processing method provided by the invention can be utilized to perform digital image correlation calculation.
The invention has the following beneficial effects:
according to the technical scheme, namely the digital image processing method provided by the invention can improve the accuracy and stability of image processing under the influence of light noise for filtering and noise reduction of the digital image under the interference of ambient light, thereby further expanding the application scene of digital image acquisition.
Optionally, in step S1, a digital image under the interference of the ambient light is acquired by using a camera.
Alternatively, in step S2, the ambient light interference region is determined according to the jump of the gray scale information, the light intensity of the ambient light is determined according to the size of the jump, and the position and size of the image of the ambient light interference region are determined according to the light intensity and the ambient light interference region.
Alternatively, referring to fig. 2, the step S3 includes the following sub-steps:
s31: determining the area size and the gray information of the characteristic subarea;
s32: and according to the area size, performing two-dimensional discrete Fourier transform on the gray information of the characteristic subarea to obtain a processed characteristic subarea image.
Optionally, in step S32, the performing two-dimensional discrete fourier transform on the gray scale information includes:
in the formula, F (u, v) represents a fourier transform function of the characteristic subregion image, and F (x, y) represents gradation information within the characteristic subregion image; m, N is constant and represents the region size of the characteristic subregion image; i represents an imaginary number mark in Fourier transform, x and y represent area image coordinates of the characteristic sub-area, and u and v represent Fourier space coordinates after Fourier transform is carried out on the characteristic sub-area.
Alternatively, in step S33, the transformed feature subregion image is subjected to noise reduction processing by using a low-pass filter. As can be seen from fig. 3, the ambient light interference adds a large amount of noise in the frequency domain, the noise energy submerges the normal image energy information, and if the noise is not suppressed and filtered, the normal image analysis cannot be performed. As can be seen from comparing the graphs (c) and (d) in fig. 3, the frequency of the ambient light noise is a high-frequency noise with respect to the real image information, and therefore, in order to eliminate the ambient light interference noise, low-pass filtering noise reduction is performed according to the high-frequency characteristics of the ambient light noise.
For an image, a low-pass filter is used to remove the high-frequency components and remove noise, so that the image is smoothed. Using the convolution principle, one can obtain:
G(u,v)＝H(u,v)·F(u,v)
in the formula, F (u, v) represents a fourier transform function of the feature subregion image, G (u, v) represents a fourier transform of the filtered image, and H (u, v) represents a transfer function of the low-pass filter.
According to the formula, a suitable low-pass filter can be selected as the low-pass filter of the invention, and the specific details are as follows:
a commonly used low-pass filter in the frequency domain of the two-dimensional discrete fourier transform is: an Ideal Low Pass Filter (ILPF), a Butterworth low pass filter, and a steady state wavelet filter.
For optimal filtering results, a steady-state wavelet filter is used for noise filtering of ambient light noise regions.
Optionally, the low pass filter is a stationary wavelet filter.
Optionally, in step S1, the ambient light is dynamic ambient light.
Specifically, in order to verify the influence of ambient light interference noise reduction filtering on the accuracy of image correlation matching, as shown, an ambient light over-strong area is simulated on a test piece, the test piece performs rigid translation on a universal testing machine at a speed of 5mm/min, and an interference light source does not change position and direction in the whole deformation process, so that in the translation process, the strong light interference area moves reversely to the translation direction, speckle correlation matching is performed by using a noise reduction method and a noise non-reduction method in different illumination areas, and the effect of the noise reduction method on light interference and the influence on the correlation matching accuracy are compared and analyzed.
As shown in table 1, a normal illumination region without optical interference is used as a displacement reference, a noise reduction correlation method and a correlation method without noise reduction processing are respectively used for image correlation matching, three-dimensional displacement of a matching reconstruction result is calculated and compared with the displacement reference, it can be seen that the optical noise reduction method has a very strong inhibition effect on matching reconstruction deviation caused by environmental strong optical interference, for an interference light intensity transition region, the reconstruction precision can be improved by 47%, the reconstruction precision for a strong optical interference center region is improved by 70%, the mean value of the reconstruction displacement deviation for the optical intensity transition region is 0.01mm, the displacement deviation in a 5mm displacement range is less than 0.02mm, the mean value of the reconstruction displacement deviation for the strong optical interference center region is 0.068mm, and the displacement deviation in the 5mm displacement range is less than 0.104 mm.
For the actual ambient light interference image, the noise reduction method is used for carrying out relevant matching on the ambient light interference image, and the result is compared with the result without the noise reduction method, so that the effectiveness of the method in the actual light interference environment is proved.
TABLE 1 optical noise reduction method Displacement deviation comparison (Unit: mm)
The displacement value is a displacement calculation result of the area without optical interference; the displacement deviation is an absolute deviation between the displacement amount and the displacement value.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (8)
1. A digital image processing method, characterized in that it comprises:
s1: a digital image under ambient light interference is acquired,
s2: obtaining an ambient light interference area image of the digital image according to the gray scale information of the digital image;
s3: acquiring a characteristic subregion image in the ambient light interference region image;
s4: carrying out noise reduction processing on the characteristic subregion image to obtain a new characteristic subregion image;
s5: and carrying out normalization processing on the new characteristic subregion image and the reference subregion image to obtain a new digital image.
2. The digital image processing method according to claim 1, wherein in step S1, a digital image under the interference of ambient light is acquired by a camera.
3. The digital image processing method according to claim 1, wherein in step S2, the ambient light interference region is determined according to the jump of the gray scale information, the intensity of the ambient light is determined according to the magnitude of the jump, and the position and the magnitude of the image of the ambient light interference region are determined according to the intensity and the ambient light interference region.
4. The digital image processing method according to claim 1, wherein said step S3 includes the sub-steps of:
s31: determining the area size and the gray information of the characteristic subarea;
s32: and according to the area size, performing two-dimensional discrete Fourier transform on the gray information of the characteristic subarea to obtain a processed characteristic subarea image.
5. The digital image processing method according to claim 4, wherein said step S32, performing two-dimensional discrete Fourier transform on said gray-scale information comprises:
in the formula, F (u, v) represents a fourier transform function of the characteristic subregion image, and F (x, y) represents gradation information within the characteristic subregion image; m, N is constant and represents the region size of the characteristic subregion image; i represents an imaginary number mark in Fourier transform, x and y represent area image coordinates of the characteristic sub-area, and u and v represent Fourier space coordinates after Fourier transform is carried out on the characteristic sub-area.
6. The digital image processing method according to claim 1, wherein in step S5, the transformed feature subregion image is subjected to noise reduction processing using a low pass filter.
7. The digital image processing method of claim 6, wherein the low pass filter is a stationary wavelet filter.
8. The digital image processing method according to any of the claims 1-7, wherein in step S1, the ambient light is dynamic ambient light.
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