CN111429370A - Method and system for enhancing images in coal mine and computer storage medium - Google Patents
Method and system for enhancing images in coal mine and computer storage medium Download PDFInfo
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Abstract
The invention relates to a method and a system for enhancing images under a coal mine and a computer storage medium.
Description
Technical Field
The application belongs to the technical field of image enhancement, and particularly relates to a method and a system for enhancing an image in a coal mine, and a computer storage medium.
Background
At present, the output value of the coal industry occupies a considerable proportion in the industrial industry of China, and with the promotion of the supervision means of coal mine safety production, the mine safety problem also arouses wide attention of the nation and the society.
A video monitoring system and a video analysis system under a coal mine are effective means for knowing the position, state and other information of moving targets such as underground personnel, equipment, vehicles and the like. However, due to the influence of dust in the underground coal mine, low illumination and other environments, the monitoring effect of the finally received image is not ideal, and the effect of the image directly influences the control of the underground condition and the subsequent analysis.
The current detection of underground coal mine targets based on image enhancement has important significance for maintaining mine safety, but the current image enhancement technology is difficult to apply in actual complex environments. Due to the fact that the stealth capability of the underground target is strong, signals radiated by the target in the underground complex environment are weak and are easily interfered by the background, and therefore part of the target is mixed in clutter below a background line. Targets in video images acquired in the complex underground environment are still weak, and actual underground 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 researched 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, special conditions such as poor light, uneven illumination, much dust and the like exist, and image shadows, dark areas, dark light, high light and the like caused by the complex illumination condition cause that the method for directly processing the pixel gray value by adopting the spatial domain enhancement and the method for realizing the enhancement by adopting the frequency domain filter to process the frequency domain image cannot meet the requirements of eliminating noise and enhancing details of the underground video image.
Through research, the phenomena of color distortion, edge blurring, halo and the like easily occur after enhancement in the current mainstream image enhancement technology.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the method and the system for enhancing the underground images of the coal mine and the computer storage medium are provided for solving the problems that the image enhancement technology adopted for the underground images of the coal mine in the prior art is easy to generate phenomena of color distortion, edge blurring, halation and the like after enhancement.
The technical scheme adopted by the invention for solving the technical problems is as follows:
the embodiment of the invention improves a multiscale Retinex (MSR) algorithm, a bilateral filter algorithm is fused in the traditional MSR algorithm, a weight factor of the bilateral filter function is taken as a central surrounding function of the MSR algorithm, an MSR algorithm model is constructed, and the MSR algorithm model is applied to the enhancement processing of a brightness component in an HSV (hue, saturation, value) space, so that the phenomena of blurred image edges and easiness in halo generation are effectively relieved, the original color saturation of the image is maintained, and the brightness and contrast details of the image are enhanced.
The image enhancement method provided by the first aspect of the invention specifically comprises the following steps:
converting the collected underground coal mine image from an RGB space to an HSV space, and extracting a brightness component, a saturation component and a chromaticity component;
taking the weight factor of the bilateral filter function as the center surrounding function of the MSR algorithm, providing the MSR algorithm fused with the bilateral filter, and constructing an improved MSR algorithm model
Carrying out image enhancement processing on the brightness component by adopting an improved MSR algorithm model;
keeping the hue component unchanged, and correcting the saturation component of the coal mine underground image according to the enhancement change of the brightness component;
and converting the underground coal mine image subjected to brightness component enhancement and saturation component correction into an RGB space from an HSV space.
The image enhancement system provided by the embodiment of the invention is provided with an image enhancement module, the image enhancement module fuses bilateral filtering in an MSR algorithm, and a weight factor of a bilateral filtering function is taken as a central surrounding function of the MSR algorithm, so that the phenomena of edge blurring and easiness in occurrence of halation in a Retinex algorithm are effectively relieved, the original color saturation of an image is kept, and the brightness and contrast details of the image are enhanced.
The coal mine underground image enhancement system provided by the second aspect of the invention specifically comprises:
the HSV space conversion module is used for converting the original underground coal mine image from the RGB space to the 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 filter function as a central surrounding function of the MSR algorithm, providing the MSR algorithm fused with the bilateral filter function 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 hue 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 into an RGB space from an HSV space.
A third aspect of the present invention provides a computer storage medium having a computer program stored thereon, which, when executed by a processor, is configured to implement the image enhancement method according to the embodiment of the first aspect of the present invention.
The invention has the beneficial effects that: by adopting the image enhancement method, the brightness and the contrast of the image can be obviously improved and the problems of image halo, edge blurring, color distortion and the like can be effectively solved after the acquired underground coal mine image is subjected to image enhancement processing under the conditions of haze and complex environments of suspended particles, water mist, coal mine dust, low illumination, inverse strong light and the like on the underground coal mine working surface.
According to the invention, through the improved multi-scale Retinex algorithm fusing bilateral filtering, the effects of video monitoring and video analysis in the underground coal mine are improved, and decision support is provided for safe production of the mine.
According to the invention, the correction function is added into the bidirectional filtering algorithm, so that the condition of illumination component estimation deviation is improved, the illumination component obtained by estimation is more accurate, and the phenomena of edge blurring and color distortion of the enhanced image are further improved.
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The technical solution of the present application is further explained below with reference to the drawings and the embodiments.
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 according to an embodiment of the present application.
Detailed Description
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
The technical solutions of the present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Example 1
The embodiment provides a method for enhancing an image in a coal mine, as shown in fig. 1, the method includes:
s1, converting the collected underground coal mine image from RGB space to HSV space, and extracting brightness component, saturation component and chroma component;
s2, taking the weight factor of the bilateral filter function as the center surrounding function of the MSR algorithm, providing the MSR algorithm fused with the bilateral filter function, 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 hue component unchanged, and correcting the saturation component of the image according to the enhancement change of the brightness component;
and S5, converting the underground coal mine image subjected to brightness component enhancement and saturation component correction from an HSV space to an RGB space.
The image enhancement method of the embodiment aims at the underground images of the coal mine, and solves the problems of image halo, edge blurring, color distortion and the like in 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 dust, low illumination and other factors, 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 illumination image can be enhanced more pertinently, and the problems of image halo, edge blurring, color distortion and the like are solved.
Further, the specific implementation mode of converting the collected underground coal mine image from the RGB space to the HSV space and 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 according to the intuitive nature of color, consisting of three attribute components, hue (H), saturation (S), and brightness (V).
The method has the advantages that the underground coal mine image is converted from the RGB space to the HSV space, specific attribute components can be selected for reinforcement when the image is enhanced, the attribute which does not need to be enhanced is reserved, and the image enhancement effect is facilitated.
Further, the MSR algorithm model fused with bilateral filtering constructed in this embodiment is:
wherein, IV(x, y) represents an original image of the luminance component; hk(x, y) is a weighting factor of the bilateral filter function, namely a center surrounding function; log (I)V(x,y)*Hk(x, y)) represents an illumination component; k represents the number of scales, k is 1,2,3 … N; w is akRepresents a weight coefficient corresponding to each scale, and
optionally, the center surround function determined according to the bilateral filtering in this embodiment may be:
wherein, tau is a correction function, f (x, y) represents the gray value at the pixel point (x, y) to be enhanced, and f (x)o,yo) Image center point (x)o,yo) Gray value of (a)rIs standard deviation, sigma, in the space domain of the Gaussian functiondIs the standard deviation in the Gaussian function value domain, m is a constant, ΔfAnd representing the difference of the gray value of the pixel point to be enhanced and the gray value of the central point of the image.
The bilateral filter function belongs to a nonlinear filter function, and the center surround function of the embodiment adopts bilateral filter, so that the edge storage can be realized, the good effect on storing the edge information of the image is achieved, and the distortion phenomenon occurring in the image enhancement can be effectively removed.
In this embodiment, it is considered that for two pixels with the same or similar gray scale values in the image, the center surround function changes due to the difference of the coordinate positions of the pixels, which finally results in the deviation of the estimation of the illumination component. According to the embodiment, the condition of the illumination component estimation deviation is improved by adding the correction function tau according to the bilateral filtering theory. The method specifically 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 sigmad/4, then
If the difference between the gray value of the pixel point to be enhanced and the gray value of the image center point is larger than sigmadAnd 4, the tau is 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 the present embodiment includes:
and S31, performing convolution operation on the central surrounding function obtained according to the weight factor of the bilateral filter 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 the RGB space to the HSV space, the brightness component V is extracted, and the image enhancement is carried out on the brightness component V by adopting the MSR algorithm of fusion bilateral filtering.
The present embodiment is based on the original image I of the extracted luminance component VV(x, y), and a center-surround function in the MSR algorithm model with fusion bilateral filtering to estimate the illuminated illumination component L of the objectV(x, y), specifically, may be:
LV(x,y)＝log(IV(x,y)*Hk(x,y))
denoted by a convolution operation.
S32, according to the MSR algorithm of this embodiment, the illumination component is removed from the original image of the luminance component, the reflection component of the object itself is decomposed, and the image of the reflection component is output, so as to enhance the image of the luminance component.
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 object can be obtained by removing the illumination component in an original image, so that the effect of image enhancement can be achieved.
According to the MSR algorithm model in the embodiment, the reflection component r can be obtainedV(x, y) adding rV(x, y) is converted from the logarithmic domain to the real domain, resulting in an enhanced output image RV(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:
since the saturation changes when the luminance component V is enhanced, the saturation component S is corrected. The existing methods for correcting the saturation component, such as linear stretching and histogram equalization, are prone to image distortion, and the embodiment improves the correction algorithm of the saturation component to avoid the image distortion.
Optionally, the method for correcting the saturation component in this embodiment may be:
Sc＝S+b(Vc-V)×
wherein S iscRepresents the corrected saturation component;
Vcrepresenting enhanced luminance components of an image
V represents the original luminance component;
b is a constant;
to adjust the coefficients.
Wherein, the adjustment coefficient can be expressed as:
V(x, y) represents the brightness variance of the pixel point (x, y) to be corrected;
S(x, y) represents the saturation variance of the pixel point (x, y) to be corrected;
(i, j) represents the pixel coordinates within the neighborhood of n × n for pixel (x, y) to be corrected.
Furthermore, 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 enhancement of the underground coal mine low-illumination image is completed.
The method for converting the image from the HSV space to the RGB space in the embodiment is as follows:
wherein p is v × (1-s), q is v × (1-f × s), and t is v × (1- (1-f) × s);
r, G and B respectively represent specific values of R, G and B components in the corresponding RGB space, and H, S and V respectively represent specific values of H, S and V components in the corresponding HSV space.
The method of the embodiment of the invention is adopted to enhance the images collected in outdoor haze days and the images collected in complex environments under coal mines, and the effect of the image enhancement algorithm of the invention is verified through parameters such as mean value, standard deviation, peak signal to noise ratio (PSNR), information entropy and the like, specifically please refer to tables 1 and 2.
The standard deviation is used for evaluating the contrast of the image, and the contrast is larger when the standard deviation is larger; the mean value represents the brightness of the image, the larger the mean 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 distortion of the image is smaller; the information entropy reflects the information content of a picture, and the larger the information entropy is, the larger the information content possessed by the picture is.
TABLE 1 outdoor haze sky image enhancement
TABLE 2 coal mine underground working surface image enhancement effect
As can be seen from tables 1 and 2, compared with the existing MSR algorithm, the image enhancement algorithm of the present embodiment improves the mean value, the standard deviation, the PSNR, the information entropy, and the like, and the image enhanced by the present invention has obvious improvements in the aspects of enhancing the brightness and the contrast, removing noise, resisting distortion, and the like.
Example 2:
the embodiment provides an image enhancement system in pit in colliery, includes:
the HSV space conversion module is used for converting the original underground coal mine image from the RGB space to the 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 filter function as a central surrounding function of the MSR algorithm, providing the MSR algorithm fused with the bilateral filter function 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 hue 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 into an RGB space from an HSV space.
For a specific implementation manner of each module, reference may be made to embodiment 1 in this embodiment.
Further, the MSR algorithm model constructed in this embodiment is:
center surround function H in the present embodimentk(x, y) is:
f (x, y) represents the gray value at the pixel point (x, y) to be enhanced, and f (x) represents the gray value at the pixel point (x, y) to be enhancedo,yo) Image center point (x)o,yo) Gray value of (a)rIs standard deviation, sigma, in the space domain of the Gaussian functiondIs the standard deviation in the Gaussian function value field, IV(x, y) denotes the original of the luminance componentImage, Hk(x, y) is a weighting factor of the bilateral filter function, namely a center surrounding function; τ is a correction function, log (I)V(x,y)*Hk(x, y)) represents an illumination component; k represents the number of scales, k is 1,2,3 … N; w is akRepresents a weight coefficient corresponding to each scale, and
in this embodiment, it is considered that for two pixels with the same or similar gray scale values in the image, the center surround function changes due to the difference of the coordinate positions of the pixels, which finally results in the deviation of the estimation of the illumination component. According to the embodiment, the condition of the illumination component estimation deviation is improved by adding the correction function tau according to the bilateral filtering theory. The method specifically 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 sigmad/4, then
If the difference between the gray value of the pixel point to be enhanced and the gray value of the image center point is larger than sigmadAnd 4, then τ is 1, namely:
where τ is a correction function, m is a constant, ΔfAnd representing the difference of the gray value of the pixel point to be enhanced and the gray value of the central point of the image.
Further, the image enhancement module comprises:
the illumination component calculation unit is used for inputting the extracted original image of the brightness component into an improved MSR algorithm model, and performing 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 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, outputting the image of the reflection component and realizing 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:
Sc＝S+t(Vc-V)×
wherein S iscRepresenting the corrected saturation component of the image, VcRepresenting the enhanced luminance component of the image, V representing the original luminance component; t is a constant and is an adjustment coefficient;
The specific implementation method of image enhancement in this embodiment can refer to embodiment 1, and is not described herein again.
Example 3:
the present embodiment provides a computer storage medium having a computer program stored thereon, which when executed by a processor, is configured to implement the image enhancement method according to embodiment 1 of the present invention.
Please refer to embodiment 1 for a specific image enhancement method, which is not described herein again.
In light of the foregoing description of the preferred embodiments according to the present application, it is to be understood that various changes and modifications may be made without departing from the spirit and scope of the invention. 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 the claims.
As will be appreciated by one skilled in the art, 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 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.
Claims (10)
1. A coal mine underground image enhancement method is characterized by comprising the following steps:
converting the collected underground coal mine image from an RGB space to an HSV space, and extracting a brightness component, a saturation component and a chromaticity component;
taking the weight factor of the bilateral filter function as a central surrounding function of the MSR algorithm, providing the MSR algorithm fused with the bilateral filter function, and constructing an improved MSR algorithm model;
carrying out image enhancement processing on the brightness component by adopting an improved MSR algorithm model;
keeping the hue component unchanged, and correcting the saturation component of the coal mine underground image according to the enhancement change of the brightness component;
and converting the underground coal mine image subjected to brightness component enhancement and saturation component correction into an RGB space from an HSV space.
2. The method for enhancing the image under the coal mine according to claim 1, wherein the improved MSR algorithm model is as follows:
wherein, IV(x, y) represents an original image of the luminance component; hk(x, y) is a weighting factor of the bilateral filter function, namely a center surrounding function; log (I)V(x，y)*Hk(x, y)) represents an illumination component; r isV(x, y) represents a reflection component; k represents the scale number of the MSR algorithm, and k is 1,2,3 … N; w is akRepresents a weight coefficient corresponding to each scale, and
3. the method of enhancing images in a coal mine well according to claim 2, wherein the center-surround function is:
tau is a correction function, f (x, y) represents the gray value at the pixel point (x, y) to be enhanced, and f (x) iso，yo) Image center point (x)o，yo) Gray value of (a)rIs standard deviation, sigma, in the space domain of the Gaussian functiondIs the standard deviation in the Gaussian function value domain, m is a constant, ΔfAnd representing the difference of the gray value of the pixel point to be enhanced and the gray value of the central point of the image.
4. The method for enhancing the image under the coal mine according to claim 3, wherein the step of performing image enhancement processing on the brightness component by using the improved MSR algorithm model comprises the following steps:
inputting the extracted original image of the brightness component into an improved MSR algorithm model, and performing 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, resolving the reflection component of the object, and outputting the image of the reflection component to realize the image enhancement of the brightness component.
5. The method for enhancing the image under the coal mine according to claim 1, wherein the method for correcting the saturation component comprises the following steps:
Sc＝S+t(Vc-V)×
wherein S iscRepresenting the corrected saturation component of the image, VcRepresenting the enhanced luminance component of the image, V representing the original luminance component; t is a constant and is an adjustment coefficient, and omega represents a neighborhood window with a pixel point (x, y) to be corrected as a center;
representing the average value of the brightness of all the pixel points in the neighborhood of the pixel point (x, y) to be corrected,
representing the average value of the saturation of all pixel points in the neighborhood range of the pixel point (x, y) to be corrected;V(x, y) represents the brightness variance of the pixel point (x, y) to be corrected,S(x, y) represents the saturation variance of the pixel point (x, y) to be corrected, and (i, j) represents the pixel point coordinate in the neighborhood range of the pixel point (x, y) to be corrected.
6. An image enhancement system in a coal mine is characterized by comprising:
the HSV space conversion module is used for converting the original underground coal mine image from the RGB space to the 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 filter function as a central surrounding function of the MSR algorithm, providing the MSR algorithm fused with the bilateral filter function 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 hue 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 into an RGB space from an HSV space.
7. The method of enhancing images in a coal mine well according to claim 6, wherein the improved MSR algorithm model is:
wherein, IV(x, y) represents an original image of the luminance component; hk(x, y) is a weighting factor of the bilateral filter function, namely a center surrounding function; log (I)V(x，y)*Hk(x, y)) represents an illumination component; k represents the number of scales, k is 1,2,3 … N; w is akRepresents a weight coefficient corresponding to each scale, and
f (x, y) represents the gray value at the pixel point (x, y) to be enhanced, and f (x) represents the gray value at the pixel point (x, y) to be enhancedo，yo) Image center point (x)o，yo) Gray value of (a)rIs standard deviation, sigma, in the space domain of the Gaussian functiondIs the standard deviation in the Gaussian function value domain, tau is the correction function, m is a constant, deltafAnd representing the difference of the gray value of the pixel point to be enhanced and the gray value of the central point of the image.
8. The coal mine underground image enhancement system of claim 6, wherein the image enhancement module comprises:
the illumination component calculation unit is used for inputting the extracted original image of the brightness component into an improved MSR algorithm model, and performing 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 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, outputting the image of the reflection component and realizing the image enhancement of the brightness component.
9. The method of enhancing images in a coal mine well according to claim 6, wherein the image correction module is further configured to construct a saturation component correction model, and the saturation component correction model is:
Sc＝S+t(Vc-V)×
wherein S iscRepresenting the corrected saturation component of the image, VcRepresenting the enhanced luminance component of the image, V representing the original luminance component; t is a constant and is an adjustment coefficient;
10. A computer storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, is adapted to implement the image enhancement method of any of claims 1-5.
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