CN111429370B - Underground coal mine image enhancement method, system and computer storage medium - Google Patents
Underground coal mine image enhancement method, system and computer storage medium Download PDFInfo
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Abstract
The application relates to a coal mine underground image enhancement method, a system and a computer storage medium.
Description
Technical Field
The application belongs to the technical field of image enhancement, and particularly relates to an image enhancement method, an image enhancement system and a computer storage medium for underground coal mines.
Background
At present, the production value of the coal industry occupies a great part of proportion in the industrial industry of China, and along with the improvement of the supervision means of coal mine safety production, the mine safety problem also draws the wide attention of the China and society.
The video monitoring system and the video analysis system in the coal mine are effective means for knowing the position, state and other information of underground personnel, equipment, vehicles and other moving targets. However, the monitoring effect of the finally received image is not ideal due to the influence of the environments such as underground coal mine dust, low illumination and the like, and the effect of the image has direct influence on the underground condition.
The current detection of the underground coal mine target based on image enhancement has important significance for maintaining mine safety, but the current image enhancement technology is difficult to apply in a practical complex environment. Because of the strong stealth capability of the underground target, the signal radiated by the target in the underground complex environment is weak and is easy to be interfered by the background, so that part of the target is mixed in the clutter below the background line. The targets in the video images acquired in the underground complex environment are still very weak, and the underground practical application is still difficult to meet.
The image enhancement under the current complex environment mostly adopts single spatial domain enhancement or frequency domain enhancement, and is mainly studied in the aspects of enhancing image contrast, projecting image details, eliminating noise and the like. However, the underground illumination condition of the coal mine is complex, and special conditions such as poor light, uneven illumination, more dust and the like exist, and the image shadow, the bright and dark area, the dim light, the high light and the like caused by the complex illumination condition can not meet the requirements of eliminating noise and enhancing details of the underground video image due to the adoption of a method for directly processing pixel gray values by spatial domain enhancement and a method for processing frequency domain images by a frequency domain filter.
Through investigation, the current mainstream image enhancement technology is easy to generate the phenomena of color distortion, edge blurring, halation and the like after enhancement.
Disclosure of Invention
The application aims to solve the technical problems that: the method and the system for enhancing the underground coal mine image and the computer storage medium are provided for solving the problems that the image enhancement technology adopted for the underground coal mine image in the prior art is easy to generate color distortion, edge blurring, halation and the like after enhancement.
The technical scheme adopted for solving the technical problems is as follows:
the embodiment of the application improves a Multi-Scale Ret-index (MSR) algorithm, a weight factor of a bilateral filtering function is used as a center surrounding function of the MSR algorithm by fusing the bilateral filtering algorithm in the traditional MSR algorithm, an MSR algorithm model is constructed, the MSR algorithm model is applied to enhancement processing of brightness components in HSV space, the phenomena of blurred edges and halation are effectively relieved, original color chroma of an image is maintained, and brightness and contrast details of the image are enhanced.
The image enhancement method provided by the first aspect of the application specifically comprises the following steps:
converting the collected underground coal mine image from RGB space to HSV space, and extracting brightness component, saturation component and chromaticity component;
taking the weight factor of the bilateral filtering function as the center surrounding function of the MSR algorithm, providing the MSR algorithm fused with bilateral filtering, and constructing an improved MSR algorithm model
Adopting an improved MSR algorithm model to carry out image enhancement processing on the brightness component;
keeping the tone component unchanged, and correcting the saturation component of the underground coal mine image according to the enhancement change of the brightness component;
the coal mine underground image subjected to brightness component enhancement and saturation component correction is converted into RGB space from HSV space.
The image enhancement system of the embodiment of the application is provided with an image enhancement module, the image enhancement module fuses double-sided filtering in an MSR algorithm, and takes a weight factor of a double-sided filtering function as a center surrounding function of the MSR algorithm, so that the phenomena of blurred edges and easy halation in a Retinex algorithm are effectively relieved, the original color saturation of an image is maintained, and the brightness and contrast details of the image are enhanced.
The image enhancement system for coal mine underground provided by the second aspect of the application specifically comprises:
the HSV space conversion module is used for converting an original underground coal mine image from an RGB space to an HSV space and extracting a brightness component, a saturation component and a chromaticity component;
the MSR algorithm improvement module is used for taking the weight factor of the bilateral filtering function as a center surrounding function of the MSR algorithm, providing the MSR algorithm fused with the bilateral filtering, and constructing an improved MSR algorithm model;
the image enhancement module is used for carrying out image enhancement processing on the brightness component by adopting an improved MSR algorithm model;
the image correction module is used for keeping the tone component unchanged and carrying out image correction on the saturation component;
and the RGB space conversion module is used for converting the underground coal mine image subjected to brightness component enhancement and saturation component correction from HSV space to RGB space.
A third aspect of the present application provides a computer storage medium having stored thereon a computer program for implementing the image enhancement method according to the embodiment of the first aspect of the present application when the computer program is executed by a processor.
The beneficial effects of the application are as follows: by adopting the image enhancement method, the brightness and contrast of the collected underground coal mine images can be obviously improved and the problems of halation, edge blurring, color distortion and the like of the images can be effectively treated after the collected underground coal mine images are subjected to image enhancement treatment under the condition that the haze day and the underground coal mine working face have complex environments such as suspended particulate matters, water mist, coal mine dust, low illumination, reverse strong light and the like.
According to the application, through an improved multi-scale Retinex algorithm integrating bilateral filtering, the effects of underground video monitoring and video analysis of the coal mine are improved, and decision support is provided for mine safety production.
The application adds a correction function in the bidirectional filtering algorithm, improves the condition of illumination component estimation deviation, ensures that the estimated illumination component is more accurate, and further improves the phenomena of edge blurring and color distortion of the enhanced image.
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The technical scheme of the application is further described below with reference to the accompanying drawings and examples.
FIG. 1 is a flow chart of a method of an embodiment of the present application;
FIG. 2 is a block diagram of an improved MSR algorithm of an embodiment of the present application.
Detailed Description
It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other.
The technical scheme of the present application will be described in detail below with reference to the accompanying drawings in combination with embodiments.
Example 1
The embodiment provides a method for enhancing an image of a coal mine underground, as shown in fig. 1, including:
s1, converting an acquired underground coal mine image from an RGB space to an HSV space, and extracting a brightness component, a saturation component and a chromaticity component;
s2, taking a weight factor of the bilateral filtering function as a center surrounding function of the MSR algorithm, providing the MSR algorithm fused with bilateral filtering, and constructing an improved MSR algorithm model;
s3, adopting an improved MSR algorithm model to carry out image enhancement processing on the brightness component;
s4, keeping the tone component unchanged, and correcting the saturation component of the image according to the enhancement change of the brightness component;
s5, converting the underground coal mine image subjected to brightness component enhancement and saturation component correction from HSV space to RGB space.
The image enhancement method of the embodiment aims at the underground coal mine image and solves the problems of image halation, edge blurring, color distortion and the like of the existing image enhancement technology. In the embodiment, the MSR algorithm is improved, and the MSR algorithm is fused with bilateral filtering to realize image enhancement, so that the technical problem is solved.
Because the underground coal mine is influenced by factors such as dust, low illuminance and the like, the MSR algorithm fused with bilateral filtering is applied to the brightness component V of the HSV space, the hue component H is kept unchanged, the saturation component S of the image is corrected according to the enhancement change of the brightness component, the low illuminance image can be enhanced more pertinently, and the problems of image halation, edge blurring, color distortion and the like are overcome.
Further, the collected underground coal mine image is converted into HSV space from RGB space, and the specific implementation mode of extracting the brightness component, the saturation component and the chromaticity component is as follows:
V←max(R,G,B)
RGB is a three primary color space consisting of three primary colors of red (R), green (G), and blue (B), and HSV is a color space created from visual characteristics of colors, consisting of three attribute components, hue (H), saturation (S), and brightness (V), respectively.
The embodiment has the advantages that when the image is enhanced, specific attribute components can be selected for enhancement, the attribute which does not need enhancement is reserved, and the enhancement effect of the image is more facilitated.
Further, the MSR algorithm model with fused bilateral filtering constructed in this embodiment is as follows:
wherein I is V (x, y) represents an original image of the luminance component; h k (x, y) is the weight factor of the bilateral filter function, namely the center-around function; log (I) V (x,y)*H k (x, y)) represents an illuminance component; k represents the number of scales, k=1, 2,3 … N; w (w) k Represents the weight coefficient corresponding to each scale, and
alternatively, the center-around function determined according to the bilateral filtering in this embodiment may be:
where τ is a correction function, f (x, y) represents the gray value at the pixel point (x, y) to be enhanced, f (x) o ,y o ) Image center point (x) o ,y o ) Gray value at sigma r Standard deviation, sigma, of gaussian function in space domain d Is the standard deviation on the value range of the Gaussian function, m is a constant, and delta f Representing the difference between the gray value of the pixel point to be enhanced and the gray value of the center point of the image.
The bilateral filtering function belongs to a nonlinear filtering function, and the center surrounding function of the embodiment has the advantages that edge preservation can be achieved, good effect is achieved on preserving the edge information of an image, and distortion phenomenon occurring in image enhancement can be effectively removed.
The present embodiment considers that for two pixels with the same or similar gray values in the image, the center-surrounding function will change due to the difference of the coordinate positions of the pixels, which ultimately results in deviation in the estimation of the illumination component. According to the bilateral filtering theory, the embodiment improves the condition of illumination component estimation deviation by adding the correction function tau. The method comprises the following steps:
judging the similarity of the gray values of the pixel points and the image center point, if the difference between the gray values of the pixel points to be enhanced and the image center point is less than or equal to sigma d /4, thenIf the difference between the gray values of the pixel point to be enhanced and the image center point is larger than sigma d And/4, τ=1.
Further, as shown in fig. 2, the step of performing image enhancement processing on the luminance component by using the improved MSR algorithm model in this embodiment includes:
and S31, carrying out convolution operation on a center surrounding function obtained according to the weight factors of the bilateral filtering function and the original image of the brightness component to obtain the illumination component of the object under illumination.
In the embodiment, after the underground coal mine image is converted from an RGB space to an HSV space, a brightness component V is extracted, and the brightness component V is subjected to image enhancement by adopting an MSR algorithm fused with bilateral filtering.
The present embodiment is based on the original image I of the extracted luminance component V V (x, y), and a center-surrounding function in an MSR algorithm model fused with bilateral filtering, estimating an illumination component L of an object under illumination V (x, y), specifically, may be:
L V (x,y)＝log(I V (x,y)*H k (x,y))
* A convolution operation is shown.
S32, according to the MSR algorithm of the embodiment, the illumination component is removed from the original image of the brightness component, the reflection component of the object is decomposed, and the image of the reflection component is output, so that the image enhancement of the brightness component is realized.
The Retinex algorithm model comprises two parts, wherein the first part is an illumination component of an object under illumination, the other part is a reflection component of the object, the reflection component represents the reflection capability of the object to light, and the information of the illumination component is removed from an original image, so that the information of the object can be obtained, and the effect of image enhancement can be achieved.
According to the MSR algorithm model in the present embodiment, the reflection component r can be obtained V (x, y), r V (x, y) conversion from the logarithmic domain to the real domain, resulting in an enhanced output image R V (x,y)。
Further, the step of correcting the saturation component of the image according to the enhancement variation of the luminance component while keeping the hue component unchanged includes:
after the luminance component V is enhanced, the saturation is changed accordingly, and thus the saturation component S is corrected. At present, the methods of linear stretching, histogram equalization and the like adopted for correcting the saturation component are easy to generate conditions of image distortion and the like, and the correction algorithm of the saturation component is improved to avoid the phenomenon of image distortion.
Alternatively, the method for correcting the saturation component according to this embodiment may be:
S c ＝S+b(V c -V)×ε
wherein S is c Representing the corrected saturation component;
V c representing the enhanced luminance component of an image
V represents an original luminance component;
b is a constant;
epsilon is the adjustment coefficient.
Wherein the adjustment coefficient ε may be expressed as:
representing the average brightness value of all pixel points in the neighborhood range of the size of n multiplied by n of the pixel point (x, y) to be corrected;
representing the saturation average value of all pixel points in the neighborhood range of the size of n multiplied by n of the pixel point (x, y) to be corrected;
δ V (x, y) represents the luminance variance of the pixel point (x, y) to be corrected;
δ S (x, y) represents the saturation variance of the pixel point (x, y) to be corrected;
(i, j) represents pixel point coordinates within a neighborhood range of n×n size of the pixel point (x, y) to be corrected.
Further, in the HSV space, the brightness component of the underground coal mine image is enhanced, the saturation component is corrected, and then the image is converted into the RGB space from the HSV space, so that the underground coal mine low-illumination image enhancement is completed.
The method for converting an image from HSV space to RGB space in the embodiment is as follows:
where p=v× (1-s), q=v× (1-f×s), t=v× (1- (1-f) ×s);
r, G and B respectively represent specific numerical values corresponding to R component, G component and B component in RGB space, and H, S and V respectively represent specific numerical values corresponding to H component, S component and V component in HSV space.
The method of the embodiment of the application is adopted to carry out image enhancement processing on the images collected in outdoor haze days and the images collected in underground complex environments of coal mines respectively, and the effects of the image enhancement algorithm of the application are verified through parameters such as mean value, standard deviation, peak signal to noise ratio (PSNR), information entropy and the like, and specific reference is made to tables 1 and 2.
The standard deviation is used for evaluating the contrast of the image, and the larger the standard deviation is, the larger the contrast is; the average value represents the brightness of the image, the larger the average value is, the larger the brightness is, the peak signal-to-noise ratio is an objective standard for evaluating the fidelity of the image, and if the peak signal-to-noise ratio is larger, the smaller the distortion of the image is represented; the information entropy reflects the information quantity of one picture, and the larger the information entropy is, the larger the information quantity possessed by the picture is.
TABLE 1 outdoor haze day image enhancement effect
TABLE 2 coal mine downhole face image enhancement effect
As can be seen from tables 1 and 2, compared with the existing MSR algorithm, the image enhancement algorithm of the embodiment has improved mean value, standard deviation, PSNR, information entropy and other aspects, and the image enhanced by the method has obviously improved brightness, contrast enhancement, noise removal, anti-distortion and other aspects.
Example 2:
the embodiment provides an image enhancement system for underground coal mine, comprising:
the HSV space conversion module is used for converting an original underground coal mine image from an RGB space to an HSV space and extracting a brightness component, a saturation component and a chromaticity component;
the MSR algorithm improvement module is used for taking the weight factor of the bilateral filtering function as a center surrounding function of the MSR algorithm, providing the MSR algorithm fused with the bilateral filtering, and constructing an improved MSR algorithm model;
the image enhancement module is used for carrying out image enhancement processing on the brightness component by adopting an improved MSR algorithm model;
the image correction module is used for keeping the tone component unchanged and carrying out image correction on the saturation component;
and the RGB space conversion module is used for converting the underground coal mine image subjected to brightness component enhancement and saturation component correction from HSV space to RGB space.
For a specific implementation of each module in this embodiment, reference may be made to embodiment 1.
Further, the MSR algorithm model constructed in this embodiment is:
center surround function H in this embodiment k (x, y) is:
f (x, y) represents the gray value at the pixel point (x, y) to be enhanced, f (x) o ,y o ) Image center point (x) o ,y o ) Gray value at sigma r Standard deviation, sigma, of gaussian function in space domain d Is the standard deviation on the value range of the Gaussian function, I V (x, y) an original image representing a luminance component, H k (x, y) is the weight factor of the bilateral filter function, namely the center-around function; τ is a correction function, log (I V (x,y)*H k (x, y)) represents an illuminance component; k represents the number of scales, k=1, 2,3 … N; w (w) k Represents the weight coefficient corresponding to each scale, and
the present embodiment considers that for two pixels with the same or similar gray values in the image, the center-surrounding function will change due to the difference of the coordinate positions of the pixels, which ultimately results in deviation in the estimation of the illumination component. According to the bilateral filtering theory, the embodiment improves the condition of illumination component estimation deviation by adding the correction function tau. The method comprises the following steps:
judging the similarity of the gray values of the pixel points and the image center point, if the difference between the gray values of the pixel points to be enhanced and the image center point is less than or equal to sigma d /4, thenIf the difference between the gray values of the pixel point to be enhanced and the image center point is larger than sigma d τ=1, i.e.:
where τ is a correction function, m is a constant, Δ f Representing the difference between the gray value of the pixel point to be enhanced and the gray value of the center point of the image.
Further, the image enhancement module includes:
the illumination component calculation unit is used for inputting the original image of the extracted brightness component into the improved MSR algorithm model, and carrying out convolution operation on the original image of the brightness component and the center surrounding function to obtain an illumination component of the object under illumination;
and the reflection component calculation unit is used for removing the illumination component from the original image of the brightness component, decomposing the reflection component of the object, and outputting the image of the reflection component to realize the image enhancement of the brightness component.
Further, the image correction module is further configured to construct a saturation component correction model, where the saturation component correction model is:
S c ＝S+t(V c -V)×ε
wherein S is c Representation ofSaturation component after image correction, V c Representing the luminance component of the enhanced image, V representing the original luminance component; t is a constant, epsilon is an adjustment coefficient;
represents the average value of the brightness of all the pixels in the neighborhood range of the pixel (x, y) to be corrected,representing the saturation average value of all pixel points in the neighborhood range of the pixel point (x, y) to be corrected; delta V (x, y) represents the luminance variance, δ, of the pixel point (x, y) to be corrected S (x, y) represents the saturation variance of the pixel (x, y) to be corrected, and (i, j) represents the pixel coordinates in the neighborhood of the pixel (x, y) to be corrected.
The specific implementation method of image enhancement in this embodiment can refer to embodiment 1, and will not be described herein.
Example 3:
the present embodiment provides a computer storage medium having stored thereon a computer program for implementing the image enhancement method described in embodiment 1 of the present application when executed by a processor.
The specific image enhancement method is described in embodiment 1, and is not described herein.
With the above-described preferred embodiments according to the present application as a teaching, the worker skilled in the art could make various changes and modifications without departing from the scope of the technical idea of the present application. The technical scope of the present application is not limited to the contents of the specification, and must be determined according to the scope of claims.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations 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.
Claims (4)
1. A method of image enhancement downhole in a coal mine, comprising:
converting the collected underground coal mine image from RGB space to HSV space, and extracting brightness component, saturation component and chromaticity component;
taking the weight factor of the bilateral filtering function as a central surrounding function of the MSR algorithm, providing the MSR algorithm fused with bilateral filtering, and constructing an improved MSR algorithm model;
adopting an improved MSR algorithm model to carry out image enhancement processing on the brightness component;
keeping the tone component unchanged, and correcting the saturation component of the underground coal mine image according to the enhancement change of the brightness component;
converting the coal mine underground image subjected to brightness component enhancement and saturation component correction from an HSV space to an RGB space;
the improved MSR algorithm model is as follows:
wherein I is V (x, y) represents an original image of the luminance component; h k (x, y) is the weight factor of the bilateral filter function, namely the center-around function; log (I) V (x，y)*H k (x, y)) represents an illuminance component; r is (r) V (x, y) represents a reflection component; k represents the scale number of the MSR algorithm, k=1, 2,3 … N; w (w) k Represents the weight coefficient corresponding to each scale, and
the center-surround function is:
τ is a correction function, f (x, y) represents the gray value at the pixel point (x, y) to be enhanced, f (x) o ，y o ) Image center point (x) o ，y o ) Gray value at sigma r Standard deviation, sigma, of gaussian function in space domain d Is the standard deviation on the value range of the Gaussian function, m is a constant, and delta f Representing the difference between the gray values of the pixel points to be enhanced and the center point of the image;
the step of performing image enhancement processing on the luminance component by using the improved MSR algorithm model includes:
inputting the original image of the extracted brightness component into an improved MSR algorithm model, and carrying out convolution operation on the original image of the brightness component and the center surrounding function to obtain an illumination component of an object under illumination;
and removing the illumination component from the original image of the brightness component, decomposing the reflection component of the object, and outputting the image of the reflection component to realize the image enhancement of the brightness component.
2. An image enhancement system for a coal mine downhole, comprising:
the HSV space conversion module is used for converting an original underground coal mine image from an RGB space to an HSV space and extracting a brightness component, a saturation component and a chromaticity component;
the MSR algorithm improvement module is used for taking the weight factor of the bilateral filtering function as a center surrounding function of the MSR algorithm, providing the MSR algorithm fused with the bilateral filtering, and constructing an improved MSR algorithm model;
the image enhancement module is used for carrying out image enhancement processing on the brightness component by adopting an improved MSR algorithm model;
the image correction module is used for keeping the tone component unchanged and carrying out image correction on the saturation component;
the RGB space conversion module is used for converting the underground coal mine image subjected to brightness component enhancement and saturation component correction from HSV space to RGB space;
the improved MSR algorithm model is as follows:
wherein I is V (x, y) represents an original image of the luminance component; h k (x, y) is the weight factor of the bilateral filter function, namely the center-around function; log (I) V (x，y)*H k (x, y)) represents an illuminance component; k represents the number of scales, k=1, 2,3 … N; w (w) k Represents the weight coefficient corresponding to each scale, andf (x, y) represents the gray value at the pixel point (x, y) to be enhanced, f (x) o ，y o ) Image center point (x) o ，y o ) Gray value at sigma r Standard deviation, sigma, of gaussian function in space domain d Is the standard deviation over the Gaussian range, τ is the correction function, m is a constant, Δ f Representing the difference between the gray value of the pixel point to be enhanced and the gray value of the center point of the image.
3. The image enhancement system of claim 2, wherein the image enhancement module comprises:
the illumination component calculation unit is used for inputting the original image of the extracted brightness component into the improved MSR algorithm model, and carrying out convolution operation on the original image of the brightness component and the center surrounding function to obtain an illumination component of the object under illumination;
and the reflection component calculation unit is used for removing the illumination component from the original image of the brightness component, decomposing the reflection component of the object, and outputting the image of the reflection component to realize the image enhancement of the brightness component.
4. A computer storage medium having stored thereon a computer program for implementing the image enhancement method of claim 1 when executed by a processor.
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