CN118154489A - Infrared image enhancement system and method based on atmospheric scattering model - Google Patents
Infrared image enhancement system and method based on atmospheric scattering model Download PDFInfo
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Abstract
An infrared image enhancement system and method based on an atmospheric scattering model, wherein a normalization processing unit acquires an original infrared image and performs normalization preprocessing to obtain a normalized image; the depth-of-field dividing unit is used for dividing the normalized image into areas according to the depth-of-field length to obtain a corresponding depth-of-field image; the parameter estimation unit carries out parameter estimation on an atmospheric scattering model of the scene depth image to obtain a corresponding depth-of-field fog-free image; the enhancement restoration unit performs enhancement processing on the depth-of-field defogging image to obtain a corresponding second depth-of-field restoration image, and the second depth-of-field restoration image is overlapped to obtain a restoration image. According to the invention, the degradation process of the infrared image is described through the atmospheric scattering model, the real scene image is reflected through reasonably estimating the model parameters, the blurring effect of the infrared image is effectively eliminated, the image contrast is greatly improved, the layering sense of the image is enhanced, the detail information of the image is highlighted, and the overall visual effect of the infrared image is remarkably improved.
Description
Technical Field
The invention relates to the technical field of infrared image enhancement, in particular to an infrared image enhancement system and method based on an atmospheric scattering model.
Background
At present, due to the limitations of a thermal imaging principle, the complexity of an atmospheric transmission environment, the sensitivity of the performance of an infrared detector and other inherent factors, the original image generated by an infrared imaging system has the problems of low signal-to-noise ratio, poor contrast, blurred edge details and the like, and particularly a blurred atomization state is shown, namely, the image is covered with a thin fog layer, and a real target scene is covered under the fog layer.
The existing infrared image enhancement method is difficult to recover a great deal of scene details originally hidden in the infrared image, so that the great deal of scene details originally hidden in the infrared image are lost.
Therefore, due to the limitations of the prior art, one cannot solve this problem.
Disclosure of Invention
(One) object of the invention: in order to solve the problems in the prior art, the invention aims to provide an infrared image enhancement system and an infrared image enhancement method based on an atmospheric scattering model, which can improve the contrast of an infrared image and restore the edge detail information of the image to improve the quality of the infrared image.
(II) technical scheme: in order to solve the technical problems, the technical scheme provides an infrared image enhancement system based on an atmospheric scattering model, which comprises a normalization processing unit, a depth of field dividing unit, a parameter estimation unit and an enhancement restoration unit;
The normalization processing unit acquires an original infrared image, and performs normalization preprocessing on the original infrared image to obtain a normalized image;
the depth-of-field dividing unit is used for dividing the normalized image into areas according to the depth-of-field length to obtain a corresponding depth-of-field image;
the parameter estimation unit carries out parameter estimation on an atmospheric scattering model of the scene depth image to obtain a corresponding depth-of-field fog-free image;
The enhancement restoration unit performs enhancement processing on the depth-of-field defogging images to obtain corresponding second depth-of-field restoration images, and superimposes the second depth-of-field restoration images to obtain restoration images.
Preferably, the normalization processing unit performs data conversion on the acquired original infrared image, and converts the depth of the image from high order to low order; finding the gray scale range of an input original infrared image by using an image histogram statistical mode, wherein the gray scale range of the original infrared image is equal to the difference value between the maximum gray scale value and the minimum gray scale value; updating the gray level of an output normalized image, wherein the gray level of the output normalized image is equal to the ratio of the difference value of the gray level value of the image and the minimum gray level value to the range of the converted data range of the image and the gray level range of the original infrared image;
The original infrared image can obtain various duplicate images after being processed, and the duplicate images are normalized by the images with the same parameters to obtain normalized images with the same form.
Preferably, the depth of field dividing unit divides the distance between the imaging point or the imaging point and the photographed object from the near to the far according to the difference of the depth of field lengths between different objects and the imaging point, so as to obtain corresponding depth of field images in sequence, wherein the depth of field dividing unit comprises a first depth of field image, a second depth of field image, … … and an nth depth of field image, and n is an integer greater than 1.
Preferably, the parameter estimation unit performs deduction calculation on the atmospheric scattering model to obtain a haze-free image model containing the transmissivity parameter and the atmospheric light value parameter; according to the haze-free image model, the parameter estimation unit carries out parameter estimation on the transmittance parameters and the atmospheric light value parameters of different depth images to obtain corresponding transmittance estimation values and atmospheric light estimation values; and substituting the transmissivity estimated values and the atmospheric light estimated values of the different depth-of-field images into the haze-free image model to obtain the corresponding depth-of-field haze-free image.
Preferably, a clear haze-free image model comprising a transmittance parameter and an atmospheric light value parameter is derived from the atmospheric scattering model pattern
：/>；
Wherein: x represents the distance of the photographed object from the imaging device; Representing the actual quantity of reflected light on the surface of an object, namely a clear haze-free image; /(I) Representing an original infrared image; /(I)Represents the intensity of the ambient atmospheric light; /(I)Representing an ambient light item; /(I)Is transmittance, which means the ratio of light reflected from the surface of an object to pass through fog blocking, and is a value of 0 or more and 1 or less.
Preferably, byIt can be seen that transmittance/>And scene depth/>The relation between the transmittance in a local area is a constant value recorded as/>Performing median filtering and adding adjustable parameters to obtain a transmissivity estimation value:
；
Wherein: (x) Representing the transmittance estimation value; /(I) Representation/>Is a pixel average value of (1); /(I)Representing an original infrared image; /(I)Represents the intensity of the ambient atmospheric light; /(I)Representing the adjustment factor.
Preferably, the histogram is used for counting the brightness of the infrared image, eliminating the noise interference of high brightness in the image, and the atmospheric light estimated value is as follows:
；
Wherein: Represents the intensity of the ambient atmospheric light; /(I) Representing the illumination intensity of sky, and setting different values according to the brightness of the image,/>0 Or more and 1 or less; /(I)Representing the original infrared image.
Preferably, the ambient light estimate is derived from the transmittance estimate and the atmospheric light estimate:
；
and reversely deducing a clear depth of field fog-free image according to the ambient light estimated value and the atmospheric light estimated value:
；
Wherein: representing the illumination intensity of sky, and setting different values according to the brightness of the image,/> 0 Or more and 1 or less; /(I)Representing an original infrared image; /(I)Representation/>Is a pixel average value of (1); /(I)Representing the adjustment coefficient; /(I)Indicating the actual amount of reflected light, i.e. a clear haze-free image, at the surface of the object.
Preferably, the parameter estimation unit stores an adjustment comparison table, and the enhanced recovery unit comprises a transmission scale and an atmosphere light value scale; the transmission scale and/or the atmospheric light value scale correspond to each other in the adjustment comparison table, adjustment data corresponding to the depth of field defogging images are selected, adjustment parameters corresponding to the depth of field defogging images are obtained according to the adjustment data, and the depth of field defogging images are respectively adjusted according to the adjustment parameters to obtain a first depth of field restoration image corresponding to each depth of field defogging image; the enhancement restoration unit performs image enhancement on the first depth restoration image by using an SSR single-scale Retinex algorithm based on center surrounding, and obtains a high-frequency reflection component by removing a low-frequency illumination component of the haze-free image so as to display a second depth restoration image; and superposing the second depth of field restored images according to the sequence of the depth of field to obtain restored images.
The infrared image enhancement method based on the atmospheric scattering model is suitable for an infrared image enhancement system based on the atmospheric scattering model, and specifically comprises the following steps:
Step 1, an original infrared image is obtained by a normalization processing unit, and normalization preprocessing is carried out on the original infrared image to obtain a normalized image;
Step 2, the depth of field dividing unit carries out area division on the normalized image according to the depth of field length to obtain a corresponding depth of field image;
Step 3, the parameter estimation unit carries out parameter estimation on the atmospheric scattering model of the depth image to obtain a corresponding depth image haze-free image;
And 4, performing enhancement processing on the depth-of-field defogging images by the enhancement restoration unit to obtain corresponding second depth-of-field restoration images, and overlapping the second depth-of-field restoration images to obtain restoration images.
(III) beneficial effects: according to the infrared image enhancement system and method based on the atmospheric scattering model, the degradation process of the infrared image is described through the atmospheric scattering model, the real scene image is reflected through reasonably estimating the model parameters, the blurring effect of the infrared image is effectively eliminated, the image contrast is greatly improved, the layering sense of the image is enhanced, the detail information of the image is highlighted, and the overall visual effect of the infrared image is remarkably improved.
Drawings
FIG. 1 is a schematic diagram of an infrared image enhancement system of the present invention based on an atmospheric scattering model;
FIG. 2 is a flow chart of an infrared image enhancement method based on an atmospheric scattering model of the present invention;
FIG. 3 is a detailed flow chart of step 3 of the present invention;
FIG. 4 is a detailed flow chart of step 4 of the present invention;
FIG. 5 is a schematic diagram of an adjustment lookup table according to the present invention;
FIG. 6 is a flow chart of the operation of an embodiment of the present invention;
FIG. 7 is an original infrared image of an embodiment of the present invention;
FIG. 8 is a depth of field haze free image of an embodiment of the present invention;
fig. 9 is a restored image of an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the preferred embodiments, and more details are set forth in the following description in order to provide a thorough understanding of the present invention, but it will be apparent that the present invention can be embodied in many other forms than described herein, and that those skilled in the art will be able to make similar generalizations and deductions depending on the actual application without departing from the spirit of the present invention, and therefore should not be limited in scope by the context of this particular embodiment.
The drawings are schematic representations of embodiments of the invention, it being noted that the drawings are by way of example only and are not drawn to scale and should not be taken as limiting the true scope of the invention.
An infrared image enhancement system based on an atmospheric scattering model, as shown in fig. 1, comprises a normalization processing unit, a depth of field dividing unit, a parameter estimation unit, an enhancement restoration unit and a control unit. And the normalization processing unit performs normalization processing on the input original infrared image and outputs a normalized image. The depth-of-field dividing unit divides the normalized image into a plurality of different depth-of-field images according to different depth-of-field lengths. The parameter estimation unit comprises a transmissivity parameter estimation module and an atmosphere light value estimation module, and the transmissivity parameter estimation and the atmosphere light value estimation are carried out by substituting different depth images into the haze-free image model; the parameter estimation unit further comprises an adjustment comparison table, and adjustment data corresponding to different transmittance intervals and atmospheric light value intervals are stored in the adjustment comparison table. The enhancement restoration unit enhances the transmissivity and the atmospheric light value of the depth of field defogging image to obtain a first depth of field restoration image, and then enhances the contrast for restoration to obtain a second depth of field restoration image; and reversely superposing the second depth of field restored image according to the depth of field length to obtain a restored image. The control unit is respectively connected with the normalization processing unit, the depth of field dividing unit, the parameter estimation unit and the enhancement restoration unit, and controls the smooth operation of the whole system.
The infrared image enhancement method based on the atmospheric scattering model is suitable for the infrared image enhancement system based on the atmospheric scattering model, and as shown in fig. 2, specifically comprises the following steps:
step 1, an original infrared image is obtained by a normalization processing unit, and normalization preprocessing is carried out on the original infrared image to obtain a normalized image.
And 2, performing region division on the normalized image by a depth-of-field dividing unit according to the depth-of-field length to obtain a corresponding depth-of-field image.
And 3, performing parameter estimation on the atmospheric scattering model of the depth image by a parameter estimation unit to obtain a corresponding depth-of-field fog-free image.
And 4, performing enhancement processing on the depth-of-field defogging images by the enhancement restoration unit to obtain corresponding second depth-of-field restoration images, and overlapping the second depth-of-field restoration images to obtain restoration images.
The following detailed description describes:
the normalization processing unit obtains an original infrared image through infrared shooting or infrared thermal imaging. And carrying out normalization processing such as region stretching and the like on the obtained original infrared image to obtain a normalized image.
The infrared camera is active infrared, the object is seen at night by improving the external brightness by tens of thousands times through an infrared light source, and an original infrared image is obtained by adding an optical filter into a common camera. The infrared thermal imaging is passive infrared, the object is seen through the infrared characteristics of the object, the object which is generally higher than the natural temperature has the infrared characteristics, the infrared energy is detected in a non-contact mode and is converted into an electric signal, and then an original infrared image is generated on a display.
The region stretching refers to data conversion of an acquired original infrared image, and the bit depth of the image is converted from high order to low order. For example, from 16 bits to 8 bits, i.e., each channel is represented by a value between 0 and 255, and each of the red, green, and blue channels is represented by a value between 0 and 255. Therefore, the information to be processed is reduced, the processing speed is high, and the hardware configuration requirement is relatively low.
The normalization process refers to a process of performing a series of standard process transformations on an image to transform the image into a fixed standard form, and the obtained standard image is called a normalized image. The original infrared image can obtain various duplicate images after undergoing processing, and the duplicate images can obtain normalized images in the same form after undergoing image normalization processing of the same parameters.
For a 16-bit high-Range original infrared image, the normalization processing is an image preprocessing process, namely the original 16-bit data of the infrared image is converted into 8-bit data, the specific operation finds the gray scale Range of the input original infrared image by using an image histogram statistical mode, and the gray scale Range of the original infrared image is equal to the difference value between the maximum gray scale value and the minimum gray scale value, namely range= (maxgray-mingray); and then updating the gray level of the output normalized image, wherein the gray level of the output normalized image is equal to the ratio of the difference value of the gray level value of the image and the minimum gray level value to the Range of the converted data Range of the image and the gray level Range of the original infrared image, namely outgray = (imggray-mingray) x 255/Range, so that the normalized image is obtained.
And (2) the depth-of-field dividing unit divides the normalized image obtained in the step (1) into image layers according to the depth-of-field length to obtain a plurality of depth-of-field images corresponding to each normalized image. And in the plurality of depth images corresponding to each normalized image, each depth image corresponds to one depth interval, and the depth interval of each depth image forms the depth interval of the corresponding normalized image.
When the infrared shooting or infrared thermal imaging is carried out to obtain a video, carrying out depth of field division on each frame image of the video to obtain a plurality of depth of field images corresponding to each frame, and when the depth of field images are positioned in a certain depth of field interval, carrying out image enhancement and restoration on the adjustment data of the depth of field interval, thereby greatly reducing the calculated amount of the invention and simultaneously completing the whole image processing process more quickly.
The image layer dividing process of the normalized image specifically comprises the steps of sequentially dividing the normalized image from the imaging point or the imaging point to the direction of the object, namely from the imaging point or the imaging point to the distance between the imaging point and the object to be shot from the near to the far according to the difference of depth of field lengths between different objects and the imaging point to obtain corresponding depth of field images, wherein the corresponding depth of field images comprise a first depth of field image, a second depth of field image, … … and an nth depth of field image, and n is an integer larger than 1.
The depth of field refers to a range of distances between the front and rear of a subject measured by imaging in which a clear image can be obtained at the front of a camera lens or other imaging point. In general, the lens aperture, the lens focal length, the distance between the image point and the object are important factors influencing the depth of field, and the larger the aperture is, the shorter the depth of field is; the smaller the aperture, the greater the depth of field length. The longer the focal length of the lens, the shorter the depth of field length; conversely, the shorter the focal length of the lens, the longer the depth of field. The closer the distance between the image pick-up point and the object is, the shorter the depth of field is; the farther the image point is from the object, the longer the depth of field.
According to the invention, infrared image enhancement is carried out on different infrared images, because for the original infrared image of infrared imaging, the aperture of the lens and the focal length of the lens are fixed, so that the distances of depth of field of different objects in the obtained original infrared image are different, the blur degree of different objects in the same image layer is different, and the concentration of corresponding fog in different image layers is also different. Therefore, the normalized image is divided according to the depth of field lengths of different objects to obtain different image layers, and different depth of field images are correspondingly obtained. Therefore, parameter estimation and corresponding image enhancement are needed for different depth images, so as to obtain a restored image of the original infrared image.
More specifically, as shown in fig. 3, the specific steps of the parameter estimation unit for parameter estimation are:
Step 301, the parameter estimation unit performs deduction calculation on the atmospheric scattering model to obtain a haze-free image model containing the transmissivity parameter and the atmospheric light value parameter.
The infrared image and the visible light image are similar due to the influence of haze weather according to the characteristic and the degradation characteristic of the spectrum imaging of the infrared image, so that the original infrared image can be deblurred by adopting an atmospheric scattering model, the contrast is increased, and the quality of the original infrared image is improved.
The atmospheric scattering model comprises two parts, as shown in formula (1):
（1）。
Wherein: Represents the wavelength of incident light; /(I) The atmospheric scattering coefficient is expressed as a function of wavelength; d represents the depth of field of the scene, i.e. the distance of the target scene from the imaging system; /(I)The illumination intensity at d=0, i.e. the actual reflected light intensity of the target object; /(I)The representation d=/>The illumination intensity of the time is generally the illumination intensity of the sky; r denotes the haze-free image reflected light.The direct attenuation term is also called direct propagation, and the part is the rest part of the object surface reflected light after passing through fog attenuation; /(I)This is partly the result of refraction and reflection of atmospheric light by mist particles in the air.
As shown in formula (2), for convenience of representation and calculation, the formula (1) is simplified, so that: And obtaining the atmospheric scattering model comprising the transmissivity parameter and the atmospheric light value parameter.
（2）。
Wherein: representing an original infrared image; /(I) Representing reflected light from the scene after atmospheric attenuation; Representing the ambient light generated by scattering of various light in the surrounding environment by airborne particles. x represents the distance of the photographed object from the imaging device; /(I) Representing the actual quantity of reflected light on the surface of an object, namely a clear haze-free image; /(I)Representing the intensity of the ambient atmosphere light outside, generally assumed to be globally constant; /(I)Is transmittance, which means the ratio of light reflected from the surface of an object to pass through fog blocking, and is a value of 0 or more and 1 or less.
As shown in the formula (3), usingRepresenting the ambient light term:
（3）。
Substituting formula (3) into formula (2) to perform simplified calculation on the atmospheric scattering model, and deriving a clear and haze-free image model containing a transmittance parameter and an atmospheric light value parameter from the atmospheric scattering model as shown in formula (4) ：
（4）。
Therefore, to restore the haze-free image, only the influence of the ambient light and the reflected light attenuation needs to be removed, that is, the ambient light needs to be removed, and the attenuated reflected light needs to be compensated, that isThis expression also means that the contrast of the ir image after deblurring is stretched. Therefore, only the ambient light value and the atmospheric light value are needed to be obtained, and the haze-free image can be recovered. As can be seen from the formula (3), the ambient light is determined by the transmittance and the atmospheric light value, and thus the ambient light value can be obtained by calculating the transmittance and the atmospheric light value, and further, a clear haze-free image can be recovered.
And 302, carrying out parameter estimation on the transmissivity parameters of different depth images by a parameter estimation unit according to the haze-free image model to obtain corresponding transmissivity estimation values.
From the atmospheric scattering model comprising the transmittance parameter and the atmospheric light value parameter, it is known that: as shown in equation (5), the transmittance/>, can be obtained ：
（5）。
It can be seen that. And by/>It can be seen that transmittance/>And scene depth/>The relation between the transmittance and the light transmittance can be considered that the transmittance in a local area is a constant value recorded as/>Median filtering is carried out on two sides of the formula of the transmissivity to obtain rough estimated value/>, of the transmissivityAs shown in formula (6):
（6）。
as shown in formula (7), adding adjustable parameters for preventing the condition that the whole image of the defocused image is dark or bright Optimizing the transmittance estimation value:
（7）。
setting adjustment parameters Wherein/>Representing the adjustment coefficient,/>Range/>The infrared image processing is generally set to be 1.4-1.8, and the/>, in the inventionThe value is preferably 1.6. Will/>Normalization processing is carried out on/>Representation/>I.e./>, the darker the original infrared imageThe smaller the value of (c) increases the transmissivity of the original infrared image so that the deblurred haze-free image is not too dull. Conversely, the brighter the original infrared image, the greater the resolution of the imageThe greater the value of (c) the transmittance of the original infrared image is reduced so that the deblurred haze-free image is not too bright. Thus/>Too large results in too small a transmittance, and the deblurred haze-free image is too bright, so/>Cannot exceed 0.75, i.e./>。
The transmittance estimation value is as shown in formula (8):
（8）。
And 303, carrying out parameter estimation on the atmospheric light value parameters of different depth images by a parameter estimation unit according to the haze-free image model to obtain corresponding atmospheric light estimated values.
The invention uses visible light to estimate the atmospheric light value, combines the characteristics of infrared image, and is generally characterized by that. However, in order to eliminate the interference of high brightness noise in the image, the histogram is used for counting the brightness of the infrared image, and the highlight value accounting for a certain proportion of the image area, such as the infrared image area/>, is eliminatedCounting the number of high brightness value points/>Corresponding luminance value asSetting parameters/>; By/>Push out/>。
Therefore, as shown in formula (9), the present invention sets the atmospheric light estimated value as:
（9）。
Wherein: Representing the illumination intensity of the sky, different values can be set according to the brightness of the image,/> 0 Or more and 1 or less, the invention is provided with/>。
And 304, substituting the transmissivity estimated values and the atmospheric light estimated values of different depth of field images into the haze-free image model to obtain a corresponding depth of field haze-free image.
Substituting the estimated transmittance value and the estimated atmospheric light value into the equation (3) as shown in the equation (10) can deduce an estimated ambient light value:
（10）。
substituting the ambient light estimated value and the atmospheric light estimated value into the formula (4), and reversely deducing a clear depth of field fog-free image as shown in the formula (11):
（11）。
More specifically, as shown in fig. 4, the step of performing the image enhancement processing by the enhancement restoration unit to obtain the restored image specifically includes:
And step 401, the enhancement restoration unit adjusts the depth of field defogging image according to the adjustment comparison table to obtain a corresponding first depth of field restoration image.
The parameter estimation unit also stores an adjustment comparison table, wherein the adjustment comparison table comprises adjustment data corresponding to different transmissivity estimation value intervals and different atmosphere light estimation value intervals. The enhanced recovery unit comprises a transmission scale and an atmospheric light value scale. And the enhancement restoration unit corresponds the transmissivity parameter and the atmospheric light value parameter corresponding to the depth of field defogging image in the adjustment comparison table, and selects adjustment data corresponding to the depth of field defogging image. And respectively adjusting the depth of field defogging images according to the adjustment parameters to obtain a first depth of field restored image corresponding to each depth of field defogging image.
As shown in fig. 5, the transmission estimated value interval and the atmospheric light estimated value interval are set at a certain distance, and when the transmission scale and/or the atmospheric light value scale corresponding to the depth-of-field haze-free image falls in one interval of the corresponding transmission estimated value and/or atmospheric light estimated value, the transmission estimated value interval and/or the atmospheric light estimated value interval have corresponding adjustment data. One or more of different staining degree, saturation and brightness corresponding to each transmissivity estimation value interval; also, each atmospheric light estimated value interval corresponds to adjustment data corresponding to one or more of different coloring degrees, saturation degrees, and brightness.
The transmission scale carries out movement selection on corresponding adjustment data in the transmission estimated value interval; similarly, the atmospheric light value scale moves and selects corresponding adjustment data in the atmospheric light estimated value section. And the adjustment data selected by the intersection points of the transmission scale and the atmosphere light value scale are the adjustment parameters corresponding to the depth of field fog-free image. And carrying out image enhancement processing on the depth-of-field defogging image according to the adjustment parameters to obtain a corresponding first depth-of-field restored image.
It should be noted that, the positioning of the transmission scale and the atmospheric light value scale is not sequential, that is, the transmission scale is compared with the estimated value of the transmittance of the depth of field haze-free image, the comparison is performed in the adjustment comparison table, the row/column corresponding to the interval of the estimated value of the transmittance matched with the depth of field haze-free image is selected, and the adjustment data such as the coloring degree, the saturation degree, the brightness and the like in the adjustment comparison table are positioned. And secondly, selecting the position of the atmospheric light estimated value matched with the depth of field haze-free image from the row/column corresponding to the selected transmissivity estimated value by the atmospheric light value scale, thereby positioning the adjustment data in the adjustment comparison table. And the positioned adjustment data is used as adjustment parameters of the depth of field defogging image, and the depth of field defogging image is enhanced according to the adjustment parameters to obtain a corresponding first depth of field restored image. The method can also comprise the steps of firstly positioning through an atmospheric light value scale to obtain a row/column corresponding to the corresponding atmospheric light value, then positioning through the transmission scale, and intersecting the row/column to obtain the corresponding adjustment parameter, so that the image enhancement processing is carried out on the depth-of-field fog-free image to obtain a corresponding first depth-of-field restoration image.
The adjustment reference table may be an EXCEL table or a rectangular coordinate system, and is not particularly limited herein, as long as it is ensured that adjustment data corresponding to the transmittance or the atmospheric light value in different estimated value intervals can be displayed.
Step 402, the enhancement restoration unit performs image enhancement on the first depth restoration image by using an SSR single-scale Retinex algorithm based on center surrounding, and obtains a high-frequency reflection component by removing a low-frequency illumination component of the haze-free image, so as to present a second depth restoration image.
The Retinex theory model considers that the color information that an object can observe is determined by the reflective properties of the object itself and the intensity of illumination around the object. The illumination intensity determines the dynamic range of all pixel points in the original infrared image, and the reflection coefficient of the object determines the inherent attribute of the original infrared image. The reflected image and the illumination image are multiplied as the original infrared image as shown in equation (12):
（12）。
Wherein: representing the observed raw infrared image,/> An illumination component representing ambient illumination intensity information,A reflection component representing the inherent nature of the object itself.
Considering image processing real-time, the invention adopts SSR (SINGLE SCALE Retinex) single-scale Retinex algorithm based on center surrounding. In the single-scale Retinex algorithm based on the center-surround, the illumination componentCan be regarded as the original infrared image/>And center surround function/>As shown in equation (13):
（13）。
The center surround function Is a gaussian kernel function, convolution kernel, and the size can be expressed as: Wherein/> Is a normalization constant, ensuring that the integral inside the convolution kernel is 1, and c is the size of the convolution kernel. By the center-surround function/>The illumination component/>, can be estimatedThe low frequency component of the corresponding image is removed from the image, and the rest is the high frequency component, namely the reflection component/>And preserve edge details of the image.
And (2) taking logarithms from two sides of the Retinex theoretical model to obtain a second depth-of-field restored image positioned on different layers as shown in a formula (14):
（14）。
The basic principle of the Retinex algorithm is to estimate from the acquired infrared image to be enhanced, i.e. I (x), which is affected by the atmosphere Then find/>, according to equation (14)，/>The method is only related to the inherent attribute of the object and does not depend on a light source, so that the influence of uneven illumination intensity can be eliminated, and the image contrast enhancement effect can be realized.
And (3) performing operation processing on the first depth-of-field restored images positioned in different layers obtained in the step 401 according to the Retinex algorithm to obtain a second depth-of-field restored image corresponding to the image enhancement of the different layers.
And step 403, overlapping the different second depth-of-field restored images according to the sequence of the depth-of-field lengths to obtain a restored image.
And the superposition is to integrate the second depth-of-field restored images of different layers together according to a certain superposition sequence, so as to realize the synthesis of the images and obtain the restored image corresponding to the original infrared image.
The overlapping sequence has no specific requirement, and the overlapping can be performed from the near to the far distance between the shooting point or the imaging point and the shot object, namely from the first depth-of-field image to the nth depth-of-field image; the overlapping may be performed from far to near in terms of the distance between the imaging point or imaging point and the object, that is, from the nth depth image up to the first depth image. In the process of enhancing the primary image, the whole image is overlapped from near to far or from far to near, so that the obtained restored image corresponding to the original infrared image can be ensured, and errors can not be caused.
The overlapping mode includes but is not limited to Alpha overlapping, positive film overlapping bottom and reverse overlapping, and only needs to ensure that the overlapping is obtained by the restored image corresponding to the original infrared image.
The following detailed description describes specific embodiments thereof:
As shown in fig. 6, the normalization processing unit obtains an original infrared image through infrared thermal imaging, and performs normalization processing on the original infrared image, where the original infrared image shows a blurred and atomized state, as shown in fig. 7. The depth of field dividing unit divides the image layers according to the depth of field lengths of different objects to obtain depth of field images corresponding to the normalized images; the depth image is deblurred through an atmospheric scattering model, and the transmissivity parameter and the atmospheric light value parameter are estimated to obtain a clear depth haze-free image, as shown in fig. 8. Finally, carrying out image enhancement through the transmission scale and the atmospheric light value scale to obtain a corresponding first depth of field restored image; contrast enhancement is performed through a Retinex algorithm to obtain a corresponding second depth-of-field restored image, and different second depth-of-field restored images are overlapped according to a certain sequence to output a high-quality restored image, as shown in fig. 9.
The infrared image enhancement system and the method based on the atmospheric scattering model describe the degradation process of the infrared image, reverse the real scene image by reasonably estimating model parameters, deblur the infrared image, and obviously reduce the details of the deblurred infrared image and the white atomization state. But the infrared image has lower dark contrast ratio, the contrast ratio of the infrared image is greatly improved through a transmission scale, an atmospheric light value scale and a Retinex algorithm, the layering sense of the infrared image is enhanced, the detailed information of the infrared image is highlighted, the overall visual effect of the infrared image is obviously improved, and the infrared image has good appearance.
The foregoing is a description of a preferred embodiment of the invention to assist those skilled in the art in more fully understanding the invention. These examples are merely illustrative and the present invention is not to be construed as being limited to the descriptions of these examples. It should be understood that, to those skilled in the art to which the present invention pertains, several simple deductions and changes can be made without departing from the inventive concept, and these should be considered as falling within the scope of the present invention.
Claims (10)
1. The infrared image enhancement system based on the atmospheric scattering model is characterized by comprising a normalization processing unit, a depth of field dividing unit, a parameter estimation unit and an enhancement restoration unit; the normalization processing unit acquires an original infrared image, and performs normalization preprocessing on the original infrared image to obtain a normalized image;
The depth-of-field dividing unit divides the normalized image into areas according to the depth-of-field length to obtain a corresponding depth-of-field image;
the parameter estimation unit carries out parameter estimation on an atmospheric scattering model of the scene depth image to obtain a corresponding scene depth fog-free image;
The enhancement restoration unit performs enhancement processing on the depth-of-field defogging images to obtain corresponding second depth-of-field restoration images, and superimposes the second depth-of-field restoration images to obtain restoration images.
2. The infrared image enhancement system based on the atmospheric scattering model according to claim 1, wherein the normalization processing unit performs data conversion on the acquired original infrared image to convert the depth of the image from high order to low order; finding the gray scale range of an input original infrared image by using an image histogram statistical mode, wherein the gray scale range of the original infrared image is equal to the difference value between the maximum gray scale value and the minimum gray scale value; updating the gray level of an output normalized image, wherein the gray level of the output normalized image is equal to the ratio of the difference value of the gray level value of the image and the minimum gray level value to the range of the converted data range of the image and the gray level range of the original infrared image;
The original infrared image can obtain various duplicate images after being processed, and the duplicate images are normalized by the images with the same parameters to obtain normalized images with the same form.
3. The infrared image enhancement system based on the atmospheric scattering model according to claim 1, wherein the depth-of-field dividing unit sequentially divides the distance between the imaging point or the imaging point and the object from the near to the far according to the difference of the depth-of-field lengths between different objects and the imaging point to obtain corresponding depth-of-field images, including a first depth-of-field image, a second depth-of-field image, … …, and an nth depth-of-field image, where n is an integer greater than 1.
4. The infrared image enhancement system based on an atmospheric scattering model according to claim 1, wherein the parameter estimation unit performs a derivation calculation on the atmospheric scattering model to obtain a haze-free image model including a transmittance parameter and an atmospheric light value parameter; according to the haze-free image model, the parameter estimation unit carries out parameter estimation on the transmittance parameters and the atmospheric light value parameters of different depth images to obtain corresponding transmittance estimation values and atmospheric light estimation values; and substituting the transmissivity estimated values and the atmospheric light estimated values of the different depth-of-field images into the haze-free image model to obtain the corresponding depth-of-field haze-free image.
5. The atmospheric scattering model-based infrared image enhancement system of claim 4, wherein a clear haze-free, haze-free image model comprising a transmittance parameter and an atmospheric light value parameter is derived from the atmospheric scattering model pattern
：/>；
Wherein: x represents the distance of the photographed object from the imaging device; Representing the actual quantity of reflected light on the surface of an object, namely a clear haze-free image; /(I) Representing an original infrared image; /(I)Represents the intensity of the ambient atmospheric light; /(I)Representing an ambient light item; Is transmittance, which means the ratio of light reflected from the surface of an object to pass through fog blocking, and is a value of 0 or more and 1 or less.
6. The infrared image enhancement system based on an atmospheric scattering model of claim 4, wherein the infrared image enhancement system is composed ofIt can be seen that transmittance/>And scene depth/>The relation between the transmittance in a local area is a constant value recorded as/>Performing median filtering and adding adjustable parameters to obtain a transmissivity estimation value:
；
Wherein: (x) Representing the transmittance estimation value; /(I) Representation/>Is a pixel average value of (1); /(I)Representing an original infrared image; Represents the intensity of the ambient atmospheric light; /(I) Representing the adjustment factor.
7. The infrared image enhancement system based on an atmospheric scattering model of claim 4, wherein the histogram is used to count the brightness of the infrared image, excluding high brightness noise interference in the image, and the atmospheric light estimate is:
；
Wherein: Represents the intensity of the ambient atmospheric light; /(I) Representing the illumination intensity of sky, and setting different values according to the brightness of the image,/>0 Or more and 1 or less; /(I)Representing the original infrared image.
8. The infrared image enhancement system based on an atmospheric scattering model of claim 4, wherein the ambient light estimate is derived from the transmittance estimate and the atmospheric light estimate:
；
and reversely deducing a clear depth of field fog-free image according to the ambient light estimated value and the atmospheric light estimated value:
；
Wherein: representing the illumination intensity of sky, and setting different values according to the brightness of the image,/> 0 Or more and 1 or less; representing an original infrared image; /(I) Representation/>Is a pixel average value of (1); /(I)Representing the adjustment coefficient; /(I)Indicating the actual amount of reflected light, i.e. a clear haze-free image, at the surface of the object.
9. The infrared image enhancement system based on an atmospheric scattering model according to claim 1, wherein the parameter estimation unit stores an adjustment reference table, and the enhancement restoration unit includes a transmission scale and an atmospheric light value scale; the transmission scale and/or the atmospheric light value scale correspond to each other in the adjustment comparison table, adjustment data corresponding to the depth of field defogging images are selected, adjustment parameters corresponding to the depth of field defogging images are obtained according to the adjustment data, and the depth of field defogging images are respectively adjusted according to the adjustment parameters to obtain a first depth of field restoration image corresponding to each depth of field defogging image; the enhancement restoration unit performs image enhancement on the first depth restoration image by using an SSR single-scale Retinex algorithm based on center surrounding, and obtains a high-frequency reflection component by removing a low-frequency illumination component of the haze-free image so as to display a second depth restoration image; and superposing the second depth of field restored images according to the sequence of the depth of field to obtain restored images.
10. The infrared image enhancement method based on the atmospheric scattering model is characterized by comprising the following steps of:
Step 1, an original infrared image is obtained by a normalization processing unit, and normalization preprocessing is carried out on the original infrared image to obtain a normalized image;
Step 2, the depth of field dividing unit carries out area division on the normalized image according to the depth of field length to obtain a corresponding depth of field image;
Step 3, the parameter estimation unit carries out parameter estimation on the atmospheric scattering model of the depth image to obtain a corresponding depth image haze-free image;
And 4, performing enhancement processing on the depth-of-field defogging images by the enhancement restoration unit to obtain corresponding second depth-of-field restoration images, and overlapping the second depth-of-field restoration images to obtain restoration images.
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