CN113487505A - Infrared image mixed noise reduction method based on noise identification - Google Patents
Infrared image mixed noise reduction method based on noise identification Download PDFInfo
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Abstract
The invention discloses an infrared image mixed denoising method based on noise identification, which comprises the steps of firstly, performing a 3 x 3 median filtering filter on a near infrared image to obtain an image only containing Gaussian noise, and then performing wavelet transformation denoising processing on the image. After the secondary wave decomposition, the main information of the image is almost completely distributed in the low-frequency sub-band, and the processing of the low-frequency part is usually avoided in order to avoid the damage of important information during denoising. And performing thresholding processing on the wavelet coefficient of the high frequency region after the image decomposition, and then performing median filtering again by adopting a 5-by-5 filter. And finally, performing wavelet reconstruction by utilizing wavelet coefficients of all sub-bands to obtain a denoised image. By the mode, the mixed denoising method can effectively remove noise compared with a single denoising method aiming at the denoising effect of the collected night infrared image, the signal to noise ratio of the near infrared face image is improved, and the edge details of the image are well reserved.
Description
Technical Field
The invention relates to the technical field of infrared image processing, in particular to an infrared image mixed noise reduction method based on noise identification.
Background
Under the visible light environment, the positioning of eyes is seriously influenced by illumination, and even a face area is difficult to accurately detect at night, and the eyes cannot be accurately positioned. Near infrared light basically has no limitation of natural illumination, and can be absorbed by pupils, while irises reflect much light. Human eyes are quickly detected by using the difference, so that the real-time performance of face monitoring is realized. However, the acquired image contains infrared noise such as gauss, salt and pepper and the like, which causes image distortion due to the influence of the conversion device and environment of the acquired image. In order to obtain a near-infrared facial image with a high signal-to-noise ratio, denoising an acquired facial infrared video sequence.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides an infrared image mixed noise reduction method based on noise identification.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that:
an infrared image mixed noise reduction method based on noise identification comprises the following steps:
s1, acquiring a face infrared image containing noise and carrying out median filtering on the face infrared image;
s2, performing wavelet decomposition on the face infrared image subjected to median filtering in the step S1 to obtain a high-frequency sub-band and a low-frequency sub-band;
s3, carrying out threshold function processing on the high-frequency sub-band obtained in the step S2, and carrying out median filtering processing on the low-frequency sub-band;
and S4, fusing the high-frequency sub-band and the low-frequency sub-band processed in the step S3, and reconstructing the fused low-frequency sub-band into a noise-reduced human face infrared image.
The mixed denoising method has the beneficial effects that compared with a single denoising method, the mixed denoising method can effectively remove noise, improve the signal-to-noise ratio of the near-infrared human face image and well reserve the edge details of the image in terms of the denoising effect of the collected night infrared image.
Further, the step S1 specifically includes:
s11, constructing an n multiplied by n filtering kernel, sliding the filtering kernel on the obtained face infrared image, and aligning the midpoint of the constructed filtering kernel with the pixel point to be filtered;
s12, acquiring the gray value of the pixel point covered by the filtering kernel;
s13, distributing the gray values of the pixel points acquired in the step S12 in sequence, and calculating the median of the sequence distribution;
and S14, replacing the pixel points covered by the midpoint of the filtering kernel by using the calculated termination.
The further scheme has the advantages that the noise of the nonlinear signal is effectively inhibited, and the image information is smoother.
Further, the formula for calculating the median of the order distribution in step S13 is:
wherein lkRepresenting the size of the square filter window, pkRepresenting the percentage of interference noise points in the image pixels.
The method has the advantages that the edge detail information is well kept while the image details are not damaged. Further, a Db3 wavelet basis is selected and selected, a decomposition level N is determined, and then N layers of wavelet decomposition are carried out on the image to obtain a high-frequency sub-band and a low-frequency sub-band. And 5-by-5 template median filtering is carried out on the low-frequency part, and thresholding function processing is carried out on the high-frequency part.
The beneficial effect of the above further scheme is that the peak burr information in the high-frequency signal is removed, and the low-frequency edge characteristics are retained.
Further, the hard threshold method is calculated in the following manner:
wherein,
is the wavelet coefficient after thresholding, W is the high frequency coefficient before thresholding, and δ is the threshold.
Further, the soft threshold method is calculated in the following manner:
wherein sgn (w) is a sign function of the wavelet coefficients.
Further, the high frequency sub-band in step S3 is subjected to a compromise threshold method:
the technical scheme has the advantages that the noise such as gauss, salt and pepper can be effectively removed, the edge details of the image are well kept, the signal to noise ratio (SNR) of the image can be effectively improved, the threshold value method can be flexibly selected according to actual conditions to carry out high-frequency offspring processing, and the image which meets requirements better is obtained.
Further, the method for reconstructing the noise-reduced face infrared image in step S4 specifically includes:
and performing image reconstruction according to the low-frequency coefficient of the wavelet decomposition and the high-frequency coefficient subjected to threshold quantization processing to finish image denoising.
The further scheme has the advantages that the signal-to-noise ratio SNR of the image is improved, and the image characteristic information and the edge information are easier to extract.
Drawings
Fig. 1 is a schematic flow chart of an infrared image hybrid noise reduction method based on noise identification according to the present invention.
Fig. 2 is an exemplary diagram of wavelet decomposition and reconstruction in the near-infrared image denoising method according to the embodiment of the present invention.
FIG. 3 is a comparison graph of mixed denoising and single denoising effects in the near-infrared image denoising method according to the embodiment of the invention.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
An infrared image hybrid noise reduction method based on noise identification is shown in fig. 1, and includes the following steps:
s1, acquiring a face infrared image containing noise and carrying out median filtering on the face infrared image;
in this embodiment, step S1 specifically includes:
s11, constructing an n multiplied by n filtering kernel, sliding the filtering kernel on the obtained face infrared image, and aligning the midpoint of the constructed filtering kernel with the pixel point to be filtered;
s12, acquiring the gray value of the pixel point covered by the filtering kernel;
s13, distributing the gray values of the pixel points acquired in the step S12 in sequence, and calculating the median of the sequence distribution;
when the interference is small, a filtering window with a small size is selected, so that the detail information of the image cannot be damaged after the noise is removed; when the interference is large, a filtering window with a large size is selected to achieve a better denoising effect, and edge detail information is well kept while image details are not damaged. The formula for calculating the median of the order distribution in step S13 is thus shown as follows,
wherein lkRepresenting the size of the square filter window, pkRepresenting the percentage of interference noise points in the image pixels.
S14, replacing the pixel points covered by the midpoint of the filtering kernel by the calculated termination;
s2, performing wavelet decomposition on the face infrared image subjected to median filtering in the step S1 to obtain a high-frequency sub-band and a low-frequency sub-band;
the wavelet transformation mainly completes several important links of wavelet decomposition, thresholding processing of wavelet coefficients, reconstruction of wavelet coefficients and the like of an image in the image denoising process. The image wavelet decomposition is to select a proper wavelet base and determine a decomposition level N, and then the image is subjected to N-layer wavelet decomposition. Different wavelet basis functions differ significantly from signal analysis to signal analysis. In the present embodiment, the wavelet basis is generally selected as Db3 wavelet basis, and the wavelet transform is a local transform of space (time) and frequency, so as to fully highlight some aspects of the problem. The wavelet transform generates low-frequency and high-frequency components, and the denoising principle of the wavelet transform is to quantize the decomposed high-frequency coefficient so as to achieve the purpose of removing noise from the image.
S3, carrying out thresholding function processing on the high-frequency sub-band processed in the step S2 to obtain a high-frequency coefficient of the noise image, and carrying out median filtering processing on the low-frequency sub-band to obtain a low-frequency coefficient of the noise image;
in this embodiment, wavelet transform denoising generally processes a high-frequency component, and performs threshold processing on a decomposed high-frequency coefficient W, and the processing methods mainly include three types:
hard threshold method:
soft threshold method:
soft and hard tradeoff threshold method:
further, in the above formula, delta, W,
Respectively, a threshold value, a wavelet coefficient before threshold value processing and a wavelet coefficient after threshold value processing. The size of the threshold determines the denoising effect, if the threshold is too high, an overflow phenomenon occurs, otherwise, the noise in the image cannot be filtered. The magnitude of the threshold is proportional to the variance of the noise, and in general, the threshold takes the value of
Or δ is (3-4) σ, where σ represents the noise variance and N is the image signal length.
The image after wavelet decomposition has very small energy dispersed except in the high frequency part and most of the energy in the low frequency part, and the wavelet coefficient of the useful signal part has higher proportion than that of the noise part. Therefore, the selection of the threshold is particularly critical, and the image quality is seriously influenced. The wavelet coefficient whose absolute value is smaller than the threshold is regarded as "noise", and its value defaults to "0", and for the wavelet coefficient larger than the threshold, it needs to be reduced and then its value is taken. The peak signal-to-noise ratio (PSNR) obtained by the hard threshold function denoising is high, but the phenomenon of local jitter exists, and the peak signal-to-noise ratio obtained by the soft threshold function denoising is not as good as that obtained by the hard threshold function denoising, but the result is smoother, and the retention of image edge information is better.
And S4, fusing the high-frequency coefficient and the low-frequency coefficient processed in the step S3, and reconstructing the fused high-frequency coefficient and the low-frequency coefficient into a noise-reduced human face infrared image.
As shown in fig. 2, the process of near infrared image decomposition and reconstruction in wavelet is shown. It can be known that after wavelet decomposition, the image is a low frequency component (b) and high frequency components (c) (d) (e) in three directions, and the detail texture and mixed noise of the image exist in the high frequency components.
In the experiment, a denoising experiment is performed on the night infrared image. Firstly, noises with different intensities are superposed on the image, and then a median filtering algorithm, a wavelet transformation denoising algorithm and a mixed denoising algorithm are respectively adopted to carry out a denoising comparison experiment on the image. The denoising processing results of the algorithms are shown in fig. 3. The SNR values of the experimental results are shown in table 1.
TABLE 1 Signal to noise ratio of the experimental results
Through SNR value comparison in Table 1, the effect of selecting 5 x 5 median filter is better when selecting Db3 wavelet base and denoising at high frequency, and SNR value reaches 22.26dB, which is much higher than single wavelet transform algorithm, SNR is improved by 2.29dB when combining wavelet transform than 3 x 3 median filter, 1.85dB is improved than single 5 x 5 median filter, and 9.53dB is improved than single wavelet transform, therefore, the hybrid denoising method is selected optimally. Compared with the traditional 33 median filtering, the method has the advantage that the median filtering is improved by 24.2 percent, and obviously, the best choice is a mixed denoising method.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The principle and the implementation mode of the invention are explained by applying specific embodiments in the invention, and the description of the embodiments is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
It will be appreciated by those of ordinary skill in the art that the embodiments described herein are intended to assist the reader in understanding the principles of the invention and are to be construed as being without limitation to such specifically recited embodiments and examples. Those skilled in the art can make various other specific changes and combinations based on the teachings of the present invention without departing from the spirit of the invention, and these changes and combinations are within the scope of the invention.
Claims (8)
1. An infrared image mixed noise reduction method based on noise identification is characterized by comprising the following steps:
s1, acquiring a face infrared image containing noise and carrying out median filtering on the face infrared image;
s2, performing wavelet decomposition on the face infrared image subjected to median filtering in the step S1 to obtain a high-frequency sub-band and a low-frequency sub-band;
s3, carrying out thresholding function processing on the high-frequency sub-band processed in the step S2 to obtain a high-frequency coefficient of the noise image, and carrying out median filtering processing on the low-frequency sub-band to obtain a low-frequency coefficient of the noise image;
and S4, fusing the high-frequency coefficient and the low-frequency coefficient processed in the step S3, and reconstructing the fused high-frequency coefficient and the low-frequency coefficient into a noise-reduced human face infrared image.
2. The method for noise reduction based on noise identification according to claim 1, wherein the step S1 specifically includes:
s11, constructing an n multiplied by n filtering kernel, sliding the constructed filtering kernel on the acquired human face infrared image, and aligning the midpoint of the constructed filtering kernel with a pixel point to be filtered;
s12, acquiring the gray value of the pixel point covered by the filtering kernel;
s13, distributing the gray values of the pixel points acquired in the step S12 in sequence, and calculating the median of the sequence distribution;
and S14, replacing the pixel points covered by the middle points of the filtering kernels by the calculated median values.
3. The infrared image hybrid noise reduction method based on noise recognition according to claim 2, wherein the formula for calculating the median of the sequential distribution in step S13 is shown as follows,
where l _ k is the size of the square filter window and p _ k is the ratio of interference noise points in the pixels.
4. The infrared image hybrid noise reduction method based on noise identification as claimed in claim 3, wherein the thresholding functions for the high frequency sub-bands in step S3 include hard threshold method, soft threshold method and compromise threshold method.
5. The infrared image hybrid noise reduction method based on noise identification as claimed in claim 4, wherein the hard threshold method is calculated by:
wherein,
is the wavelet coefficient after thresholding, W is the high frequency coefficient before thresholding, and δ is the threshold.
6. The infrared image hybrid noise reduction method based on noise identification as claimed in claim 4, wherein the soft threshold method is calculated by:
wherein sgn (w) is a sign function of the wavelet coefficients.
7. The infrared image hybrid noise reduction method based on noise identification according to claim 4, wherein the compromise threshold method is calculated in a manner that:
8. the infrared image hybrid noise reduction method based on noise identification according to claim 7, wherein the reconstruction of the infrared image of the face after noise reduction in step S4 specifically includes:
and performing image reconstruction according to the low-frequency coefficient of the wavelet decomposition and the high-frequency coefficient subjected to thresholding treatment to finish the denoising of the image.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115115653A (en) * | 2022-06-13 | 2022-09-27 | 广东众志检测仪器有限公司 | Refined temperature calibration method for cold and hot impact test box |
| CN117911267A (en) * | 2023-12-28 | 2024-04-19 | 北京声迅电子股份有限公司 | Back-scattered X-ray image noise reduction method based on wavelet transformation |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115115653A (en) * | 2022-06-13 | 2022-09-27 | 广东众志检测仪器有限公司 | Refined temperature calibration method for cold and hot impact test box |
| CN117911267A (en) * | 2023-12-28 | 2024-04-19 | 北京声迅电子股份有限公司 | Back-scattered X-ray image noise reduction method based on wavelet transformation |
| CN117911267B (en) * | 2023-12-28 | 2024-08-02 | 北京声迅电子股份有限公司 | Back-scattered X-ray image noise reduction method based on wavelet transformation |
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