 Research
 Open Access
Research on denoising processing of computer video electromagnetic leakage reduction image based on fuzzy degree
 Chunwei Miao^{1}Email author
https://doi.org/10.1186/s1364001804054
© The Author(s). 2019
 Received: 20 October 2018
 Accepted: 28 December 2018
 Published: 11 January 2019
Abstract
On the basis of analyzing, receiving, and parsing the computer video electromagnetic leakage emission signal, an image of the screen display content can be obtained. Due to the interference noise existing in the receiving process, the received image information may be drifted, the recognition may be poor, and the definition might be low. In order to improve the recognizability of the restored image, firstly, based on image noise analysis, cumulative averaging and noise smoothing, this paper proposes an image processing method based on ambiguity for electromagnetic leakage emission reduction image. Secondly, according to image denoising implementation steps, combined with computer video reproduction example, the image processing method was verified by comprehensive experiments. Lastly, the image evaluation and signaltonoise ratio analysis of the experimental results were carried out. The results show that the image processing method based on the ambiguity of electromagnetic leakage emission reduction image has some improvement in subjective and objective evaluation with obvious promotion.
Keywords
 Electromagnetic leakage emission
 Fuzzy degree
 Image processing
 Image evaluation
1 Introduction
Changes in current during the operation of the computer will result in electromagnetic leakage emissions. If these electromagnetic leakage emissions are received for analysis, they may be restored with relevant information, resulting in information leakage [1–5]. Computer video information is one of the most intuitive and threatening of it. Dutch researcher Eck W Van demonstrated the experiment of video reception and restoration in 1985 and published an article in Computer & Security, detailing the technical details of the feasibility of recovering and restoring computer monitor screen information by simply modifying the TV set, which caused a huge sensation in the information security session. As the radiation performance of computer video information is relatively high, electromagnetic leakage transmission signals are more easily received, and video information becomes the most easily intercepted and reproduced red information in computer systems [6–8]. Due to the inevitable electromagnetic environment, receiving distance, equipment noise, and other factors in the process of receiving and restoring video information, a large amount of noise is introduced into the received image, which has problems such as drift, poor recognition, and low definition, which seriously affects the image recognition. Therefore, image denoising processing and visual effects improvement are particularly important. Essentially, image denoising is a way to remove the noise of a contaminated image. Common denoising algorithms are divided into spatial domain methods and transform domain algorithms. The spatial domain algorithm directly processes the pixels to separate the real signal from the noise. The transform domain algorithm firstly transforms the signal into another domain, such as the frequency domain by using a transform method; processes the representation coefficients in the transform domain according to different characteristics of the signal and noise in the transform domain; and finally inversely transforms to the image space domain. In this way, the image denoising is obtained after such process. Hwang et al. proposed an adaptive median filtering method based on the ranking order. Determining if the pixel gray value is received is the key to decide whether to use median filtering [9]. Based on this method, an improved method has emerged, using different constraints to construct the objective function to denoise the image [10]. Shibata proposes a novel misalignmentrobust joint filter based on weightvolumebased image composition and jointfilter cost volume [11]. In view of the holding unsatisfactory effects of the traditional nonlocal means algorithm for texture details, an improved nonlocal means denoising algorithm combined with fuzzy edge complement (FEC) is proposed. The edge texture feature image is detected by the FEC algorithm [12]. A new image denoising algorithm based on thresholdingbased magnitude and phase regularization of the coefficients of undecimated dualtree complex wavelet transform (UDTCWT) is proposed [13]. The abovementioned algorithms are quite significant in terms of denoising, but some of them bring some problems in the application, such as the image is too smooth, the image information is lost, and the edges become more blurred. Firstly, the paper based on image noise analysis, cumulative averaging, and noise smoothing, an image processing method based on ambiguity for electromagnetic leakage emission reduction image, is proposed. Secondly, according to image denoising implementation steps, combined with computer video reproduction example, the image processing method was verified by comprehensive experiments. Finally, the image evaluation and signaltonoise ratio analysis were carried out on the experimental results.
2 Proposed method
Image processing essentially extracts the feature quantity or special information in the image and transforms the gray level of the image to achieve the purpose of improving the signaltonoise ratio and optimizing the image quality. The most valuable thing about the electromagnetic leakage emission reduction image is the text information, which has nothing to do with color. Therefore, the process of image processing should not only effectively eliminate noise, but also improve the image quality, and also enhance the image according to the characteristics of the text to improve the visual effect [14–16].
2.1 Electromagnetic leakage emission reduction image noise analysis
More and more research results show that correct estimation and utilization of image noise have important guiding significance and use value for image denoising followup processing. No matter how good the noise removal method is, it is impossible to apply it to all noise. Electromagnetic leakage emission reduction image is limited by objective factors such as imaging mode and equipment, and the received image degradation is serious. It is inevitable to introduce Gaussian noise, shortline impulse noise, and various mixed noises. The noise itself may be related to each other and may be independent of each other, which may or may not be related to the useful signal, and exhibit different characteristics. The effect of noise on the image can be described by two different mathematical models [17]:
s(t) represents a real image signal, n(t) represents additive Gaussian noise, and x(t) represents a noisy image. Electromagnetic leakage emission reduction image since static acquisition is performed on computer video screen information; each frame of static image introduces additive noise, which is additive in nature and independent of the signal.
s(t) represents a real image signal, n(t) represents multiplicative noise, and x(t) represents a noisy image. Unpredictable noise in electromagnetic leak emission reduction images is generally a multiplicative noise.
2.2 Image denoising
2.2.1 Accumulated average
The electromagnetic leakage emission reduction image is a periodic image that is actually a period of time because it is a static image. As a periodic repeat image, the general signal is relatively stable and the correlation is better. Additive noise is generally randomly changed, and the multiframe averaging method can effectively improve the signaltonoise ratio. Although the noise in a single frame is more serious, statistically speaking, the distribution of signals is regular, and the noise of each image can be considered to be randomly and evenly distributed. Therefore, the cumulative average of multiple images can suppress the noise, to enhance the signal. After the m images are accumulated and averaged, the signaltonoise ratio can be improved by \( \sqrt{m} \). However, it is not like the more the number of images is accumulated, the better the results will be. This is because the imaging device cannot accurately capture the line synchronization and frame synchronization of the image to be received, so the images are offset between different frames. If the accumulated number of frames is too large, the average image may produce a relatively large edge blur that affects the resolution of the image detail [18].
2.2.2 Image noise smoothing
Image noise smoothing is a way to eliminate noise, both to remove noise as much as possible, and not too distorted. Commonly used algorithms for noise removal include median filtering, mean filtering, and so forth.
Median filtering
Median filtering is a nonlinear filtering technique based on sorting statistics theory to remove noise. The basic principle is to replace the value of a point in a digital image with a median value in a neighbor of the point. In the image processing, a 3 × 3 matrix is generally used as a template for image filtering, and some also adopt different shapes, such as a line shape, a circle shape, and a cross shape. It can protect the edge of the image well while removing noise, which is more effective for impulse noise, but it is not ideal for Gaussian noise median filtering.
Mean filtering
Also known as linear filtering, the main method is the neighbor averaging method. The basic principle is to replace the individual pixel values in the image to be processed with a mean. The method is to use a template with odd points to slide on the image, and the size of the template can be selected according to the actual situation. The template includes adjacent pixels around it, that is, the surrounding n pixels centered on the target pixel. The filtering process calculates the mean value of the gray value corresponding to each pixel point in the current template for the current pixel point (x, y) to be processed in the image and assigns the mean value to the current pixel point (x, y) as the gray level g(x, y) of the image at this point after processing.
Filter method selection
2.3 Image enhancement
2.3.1 Ambiguity
The fuzzy inference process uses fuzzy logic to map the membership degree of the input fuzzy set obtained by the fuzzification process to the membership degree of the output fuzzy set. The oftenused fuzzy logic has AND, OR, etc., and for the AND logic in the fuzzy set, the equivalent membership is the smallest while the OR is equivalent to the largest membership.
If it is known that the input degrees belonging to the fuzzy sets A and B are a and b, respectively, the membership of the input belonging to both A and B is MIN (a, b). Fuzzy rules are used in the process of fuzzy reasoning. Commonly used are IFTHEN structures, and fuzzy logic can be used. If the membership degree is established, the membership degree of the conclusion will be the same.
In general applications, there are multiple fuzzy rules. Each fuzzy rule determines the membership degree of the output in a certain output fuzzy set. In the overlapping part of each fuzzy set, the membership degree is taken as the maximum value, so that the final output of the membership graph can be obtained. This method of reasoning is called the minimummaximum method.
where μ(Z_{i}) is the membership degree of the output fuzzy set, and the area center of gravity method is actually the weighted average method.
2.3.2 FIRE filter principle
The fuzzy inference ruled by elseaction (FIRE) filter is a fuzzy system based on the IFTHENELSE structure, which maps the input variables nonlinearly to the output variables. In the image denoising process, the input variable is defined as the luminance difference value x_{j} = p_{j} − p (p_{j}∈W), where p is the luminance value of the processed pixel point, and W = {p_{j}} is the pixel point in the neighborhood of p brightness value. The output y is a correction value of the luminance value of the processed point.

IF (x_{1} is A_{11}) AND (x_{2} is A_{12}) …… AND (x_{M} is A_{1M}) THEN (y is PY)

IF (x_{1} is A_{21}) AND (x_{2} is A_{22}) …… AND (x_{M} is A_{2M}) THEN (y is PY)

IF (x_{1} is A_{N1}) AND (x_{2} is A_{N2}) …… AND (x_{M} is A_{NM}) THEN (y is PY)

IF (x_{1} is A^{*}_{11}) AND (x_{2} is A^{*}_{12})…… AND (x_{M} is A^{*}_{1M}) THEN (y is NY)

IF (x_{1} is A^{*}_{21}) AND (x_{2} is A^{*}_{22})…… AND (x_{M} is A^{*}_{2M}) THEN (y is NY)

IF (x_{1} is A^{*}_{N1}) AND (x_{2} is A^{*}_{N2})…… AND (x_{M} is A^{*}_{NM}) THEN (y is NY)

ELSE (y is ZY)
2.3.3 Image sharpening
In the early noise image smoothing process, the boundaries and contours in the image are often blurred. In order to reduce the influence of such unfavorable effects, it is necessary to use image sharpening technology to make the edges of the image clear, reduce the blurring effect caused by the image modification, highlight the outline of the image, and effectively improve the clarity of the text details. The more commonly used algorithms are gradient sharpening, Laplacian sharpening, and highpass filtering.
We define the input of the FIRE operator as the neighborhood grayscale difference, Δx_{j} = x_{j}(n) − x(n), and the output Δy(n) is obtained by fuzzy inference, and the final output y(n) = x(n) + Δy(n) is the corrected result.

IF(x_{1} is A) AND (x_{9} is A) THEN (y is NY)

IF(x_{2} is A) AND (x_{11} is A) THEN (y is NY)

IF(x_{8} is A) AND (x_{23} is A) THEN (y is NY)

ELSE (y is ZY)
The rule indicates that the surrounding pixel points are lower than the target point pixels, and y takes a negative value to reduce the brightness of the target pixel point; otherwise, the target pixel brightness does not change.
It can be seen that this method considers the sharpening index in all directions around the pixel and conforms to the directional characteristic of the character stroke. According to the fuzzy reasoning, if there is no sharpening, the central pixel has no change, the subjective reasoning of the fuzzy reasoning compound person, showing the effectiveness of fuzzy logic.
2.3.4 Contrast enhancement
The methods of contrast enhancement mainly include histogram modification, gradation transformation, and histogram equalization. The histogram modification technique can enhance the text information, but the noise at this time is not weakened but enhanced. The gradation transformation can change the original gray value distribution of the image, both linear and nonlinear. The gradation transformation can improve the sharpness of the image, expand or compress the entire range of the gray level of the image or one of the segments, and display the details of the image that need to be highlighted. Histogram equalization is a histogram correction method based on the cumulative distribution function transformation, which can produce an image with a uniform probability density of grayscale distribution. This is a relatively common method of contrast enhancement.
 1.
Calculate the gray histogram of the image to determine the grayscale distribution of the image.
 2.
Define a single fuzzy set dark, gray, bright, membership function as shown in the left line of Fig. 2, representing the linear function of the segment, respectively representing the size of the gray level belonging to the three fuzzy sets of dark, gray, and bright. Three membership degrees are found for the gray levels in each grayscale range.
 3.Use fuzzy inference mechanism with the following simple rules:

IF dark, THEN black;

IF gray, THEN gray;

IF bright, THEN white.

S_{1} = gmin, S_{2} = gmid, and S_{3} = gmax are the minimum, intermediate, and maximum value of gray, respectively, in the image obtained by the gray histogram, representing black, gray, and white, and is set to a single value of the fuzzy set.
The above formula can be regarded as a general weighted average method to solve the deblurring problem.
It can be seen from the above steps that the contrast enhancement method based on the fuzzy rule actually uses a fuzzy inference mechanism to create a grayscale map to implement grayscale transformation. This method considers the grayscale distribution range of the image as a whole. The gray level is fuzzy classified, and its visual effect is obviously superior to the traditional gray level equalization method.
3 Simulation experimental results and discussions
According to the above analysis, combined with the actual receiving environment of electromagnetic leakage transmission and receiving video information, we conducted experimental verification. The experimental computer uses a Lenovo desktop with a resolution of 1024 × 768. The following is the effect of combining the different images of the electromagnetic leakage emission reduction sample image of the same display video signal. The first two steps are respectively processed according to the weighted mean filtering and the blur sharpening selected by image denoising and image enhancement. In order to better compare with other algorithms, the last step is to use fuzzy enhancement, gray balance, and grayscale stretching.
 1.
Weighted mean filtering → blur sharpening → fuzzy enhancement
 2.
Weighted mean filtering → blur sharpening → gray balance
 3.
Weighted mean filtering → blur sharpening → grayscale stretching
4 Discussion
4.1 Subjective evaluation of images
The evaluation of image quality is usually divided into subjective evaluation and objective evaluation. The subjective evaluation is mainly to select a group of people to subjectively score the images before and after processing, so as to obtain the improvement degree of the image. This evaluation method can reflect the quality change of the image before and after the treatment to a certain extent, but the subjectivity is strong, and the result is related to the visual difference between the selected person and the individual, and the result is not certain. From the results of the above processing, Figs. 8 and 10 have a certain visual improvement, and the processing of Fig. 9 has decreased.
4.2 Objective evaluation of images
i and j represent the position index of the pixel, f_{i, j} and \( {\widehat{f}}_{i,j} \) respectively represent the original (no noise) image and the restored image, and M and N respectively represent the height and width of the image.
The higher the value of the peak signaltonoise ratio, the closer it will be between the result after denoising and the original image signal, and the less the distortion is in the result. It should be noted that the value of the peak signaltonoise ratio is not exactly the same as the effect of human visual perception. For some visually pleasing results, sometimes the peak signaltonoise ratio is rather small. This is because the perception of error by human vision is affected by many factors, which leads to deviations in the perception of errors.
The other is an evaluation method without a reference image. For the electromagnetic leakage emission image of the monitor video signal restored by the receiver, there is no standard original image itself, and only this evaluation method can be adopted. The following is a detailed introduction to the signaltonoise ratio evaluation method without a reference image.
The principle of the signaltonoise ratio estimation method without reference image is to first estimate the magnitude of the image noise variance, calculate the peak signaltonoise ratio (PSNR), and then estimate the improvement of the signaltonoise ratio according to the noise variance and the PSNR. The PSNR of the processed image minus the PSNR of the original image is the estimated signaltonoise ratio improvement.
The traditional estimation of noise variance is to take the flat region in the image for calculation. Let digital image noise be the signal be represented as x(n) = s(n) + u(n), the sample length be the N, where s(n) is the ideal signal, u(n) is the noise signal, and u(n) can be considered as the approximation. u(n) can be considered to approximate the Gaussian distribution of N(0, σ^{2}). For additive noise, the signal s(n) is statistically independent of the noise u(n).
If the flat area of the image can be accurately extracted, then the variance of the noise can be evaluated. In a local flat area, assuming that the noise signal is Gaussian white noise, the noise expectation is 0 and the noise variance is σ^{2}. The mean of the ideal signal is the mean of the signal, and the variance of the signal is 0.
g(i, j) is the ideal image, and I(i, j) is the noisy image. Where E is the mean value, and M and N are the size of the local area.
A very important indicator for estimating noise accuracy in this way is to accurately determine a series of flat local regions. The evaluation method first decomposes the image into nonoverlapping rectangular regions with approximately the same gray level. The basic idea is to define the consistency criterion as follows: For two adjacent regions, the gray mean values are represented by I and I + ΔI, respectively. ΔI is the difference between the two. If ΔI/I is less than a threshold T, the two regions are considered to have approximate gray levels and belong to a uniform region, which can be combined. If ΔI/I is greater than the threshold T, the two regions are regions that can be clearly separated and have a certain gradation difference. For the selection of flat local regions, a flat region can be found by region decomposition method or region merging method according to image features.
The local variance and the overall PSNR can be calculated according to Eqs. (9) and (10), where M and N is the size of the selected flat region. To maintain the robustness of the noise variance estimate, the variance used to calculate the PSNR takes the median of the variance sequences for all local regions.
4.3 Evaluation results
Noise standard deviation of various combined algorithms and improvement of signaltonoise ratio
Noise standard deviation  PSNR  Improved signaltonoise ratio  

Original image  63.5421  12.5871  
Algorithm combination 1  49.6783  15.6523  3.0652 
Algorithm combination 2  70.4327  11.4243  − 1.1628 
Algorithm combination 3  51.9754  15.3356  2.7485 
Through the analysis and evaluation of the above various image processing methods, the evaluation results of the evaluation method without reference images are basically consistent with the actual effects, but sometimes, there is a certain deviation due to the complexity of the noise. However, it provides a quantitative analysis of the objective degree of image restoration, which can reflect the degree of image improvement to some extent.
5 Conclusions
The denoising process of computer video electromagnetic leakage emission reduction image is of great significance for improving the video reproduction and reception capability. From the effect and evaluation after the denoising process, the signaltonoise ratio of the image is improved, the contrast of the image is enhanced, and to some extent, the recognizability of the image is improved.
The processing of computer video electromagnetic leakage emission reduction image is of great significance for restoring text recognition degree, which not only improves the visual effect to a certain extent, but also provides an objective method for image evaluation. However, it should also be noted that for the computer audio electromagnetic leakage emission reduction image with relatively complicated noise, it is impossible to achieve a great breakthrough by relying on the latter image processing. Therefore, the reduction of computer video information should be further started from the source, reduce the introduced noise, and further study the new adaptive image processing algorithm, such as artificial neural network (CMAC), genetic algorithm, and wavelet transform, the combination of the two can achieve better reception.
Declarations
Acknowledgements
The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions.
Funding
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Author’s contributions
The author takes part in the discussion of the work described in this paper. The author read and approved the final manuscript.
Competing interests
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