CN107767348B - Single tunnel image rapid enhancement method based on imaging model constraint - Google Patents
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Abstract
The invention relates to the technical field of image processing, and provides a single tunnel image rapid enhancement method based on imaging model constraint; the parameter estimation and image reconstruction of the imaging model can be completed within seconds, and a tunnel enhanced image with improved brightness and contrast is obtained; the method mainly comprises the following steps of: firstly, atmospheric light estimation; secondly, estimating a transmission map; and thirdly, restoring image calculation. Where the transmission map estimation and illumination map estimation calculations consume a large portion of the computation time. The scheme of the invention avoids two big data statistics and related calculation, and the tunnel image enhancement processing is completed within 3-5 seconds. The image enhancement processing speed is greatly accelerated. The processing effect meets the identification requirement of the tunnel passing condition.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to a method for rapidly enhancing a single tunnel image.
Background
The road tunnel environment is closed, and the illumination of the artificial light source is uneven. The resulting image is dark, low contrast, and noisy. The method causes difficulty in quickly and accurately identifying the road passing condition in the tunnel. Therefore, the influence of multicolor haze is effectively weakened, and the contrast of the tunnel image is enhanced as the basis of subsequent image identification. At present, although an image enhancement scheme specially aiming at tunnel image characteristics is not available, a lot of researches on image restoration in foggy days and nighttime foggy days based on imaging model estimation are carried out. Night defogging methods based on illumination estimation, such as suggested by the commander; the weighted entropy method proposed by Dubok Park et al; he Kaiming et al. The methods achieve good defogging effect in actual operation. From the technical idea, these methods are divided into two categories on the basis of imaging models. One estimates model parameters (commander, Dubok Park, etc.) such as scene light source area, illumination intensity, atmospheric light value, transmission diagram, etc. by a statistical method. The method greatly improves the brightness and the contrast of the defogged image, and has clear details and good visual effect. However, because a large amount of statistical operations are required and the processing time is dozens of seconds or more, the method is not suitable for tunnel image enhancement processing with strict real-time requirements.
The other type adds prior knowledge (He Kaiming, etc.) to obtain model additional conditions, and deduces a fixed formula to determine an illumination transmission diagram. Such as dark channel prior. In practice, the method finishes the treatment within a few seconds, and the treatment effect is good. But its application is limited due to the conditions of the dark channel. For tunnel images under artificial light source conditions, the dark channel conditions are apparently not true. Therefore, the method is not good in effect of directly applying to tunnel image enhancement. How to reduce the dependence on the prior knowledge while maintaining the rapidity of the method is an effective improvement method of the method, thereby improving the practicability of the method.
Image defogging and enhancement based on the imaging model comprise three parts of calculation. Firstly, atmospheric light estimation; secondly, estimating a transmission map; and thirdly, restoring image calculation. Some statistical methods also include illumination map estimation. Where the transmission map estimation and illumination map estimation calculations consume a large portion of the computation time. Therefore, reducing the time consumption of transmission map and illumination map estimation greatly reduces the image defogging and enhances the calculation time. On the other hand, more general additional conditions are searched for to solve the model parameter calculation formula, so that the accuracy of a certain degree is guaranteed, and the calculation is fast.
In summary, the invention provides a tunnel image transmission map fast estimation and tunnel image reconstruction algorithm based on imaging model constraints. The parameter estimation and image reconstruction of the imaging model can be completed within seconds. The tunnel enhanced image with improved brightness and contrast is obtained.
Disclosure of Invention
In view of the above, the present invention provides a single tunnel image fast enhancement method based on imaging model constraint; the parameter estimation and image reconstruction of the imaging model can be completed within seconds. The tunnel enhanced image with improved brightness and contrast is obtained.
The invention solves the technical problems by the following technical means:
a single tunnel image rapid enhancement method based on imaging model constraint comprises the following steps:
1) the atmospheric light estimation specifically comprises the following steps:
11) optimizing the whole image by using a quadtree method to determine the global atmospheric light Aglob；
12) Performing uncovered partition on the whole image, and estimating local atmospheric light by using the assumption that the local atmospheric light is a constant
13) Global atmospheric light AglobAnd estimating local atmospheric light
Combined with correction to obtain local atmospheric light correction value
2) The transmission map estimation specifically comprises the following steps:
21) deriving a local transmission map lower limit t according to imaging model constraintsiminA formula;
22) performing non-covering partition on the whole image to locally transmit the lower limit t of the imageiminBased on this, the local transfer diagram t is estimated, following the assumption that the local transfer diagram is constant, by adding the tuning parametersi；
3) Parameter modification, specifically comprising the following steps:
31) obtaining the maximum channel W of the original imagei；
32) With WiFor guiding, correction of local atmospheric light
Filtering is carried out;
33) with WiFor guidance, estimate t is made for the transmission mapiFiltering is carried out;
4) setting an illumination value to reconstruct a tunnel image, specifically comprising the following steps:
41) carrying out simulation analysis on a large amount of grasped tunnel images to determine the average illumination value L of the images to replace Li；
42) Correcting the corrected local atmospheric light
Transmission map value tiSet illumination map value L and original image value
Reconstructing tunnel images by substituting imaging models
Further, in the step 11), the gray scale image of the original image is divided into four equal parts, and the maximum average brightness is obtained; and then, quartering the area where the maximum is located, repeating optimization till 4-5 cycles, wherein the average brightness of the final area is the global atmospheric light estimated value Aglob。
Further, in the step 12), the local atmospheric light is estimated by the following formula
Where D is a local set of pixels. (r)i,gi,bi) Representing points on the corresponding color space; (r)l,gl,bl) Representing the image light source color point.
Further, in the step 13), the local atmospheric light is corrected by combining the following formula:
wherein,
as a combination coefficient, i.e.
Further, in the step 22), the local transmission map tiThe estimated formula of (c) is as follows:
wherein, tiIs a partial transmission diagram, timinFor the lower limit of the partial transmission map, θ is an independent variable timinAs a function of (c).
The single tunnel image rapid enhancement method based on the imaging model constraint has the following advantages: according to the scheme of the invention, two big data statistics and related calculation are avoided, so that the tunnel image enhancement processing is completed within 3-5 seconds, the image enhancement processing speed is greatly increased, the brightness and the contrast of the image are improved, and the processing effect meets the tunnel traffic condition identification requirement.
Detailed Description
Fig. 1 shows a flow diagram of a single tunnel image fast enhancement method based on imaging model constraints.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings.
Referring to fig. 1, the method for rapidly enhancing a single tunnel image based on imaging model constraint includes the following steps:
1) the atmospheric light estimation specifically comprises the following steps:
11) optimizing the whole image by using a quadtree method to determine the global atmospheric light Aglob(ii) a The method comprises the following specific steps: drawing the gray scale of the original imageDividing equally to obtain the maximum average brightness; and quartering the area where the maximum is located, repeating optimization until the maximum is located for proper times, and obtaining the final average brightness which is the global atmospheric light estimated value Aglob；
12) The whole image is divided into uncovered regions, and the local atmospheric light is estimated by following the assumption that the local atmospheric light is a constant
Where D is a local set of pixels. (r)i,gi,bi) Representing points on the corresponding color space; (r)l,gl,bl) Representing image light source color points;
13) global atmospheric light A is performed by the following formulaglobAnd estimating local atmospheric light
Combined correction to obtain local atmospheric light correction value
Wherein,
as a combination coefficient, i.e.
2) The transmission map estimation specifically comprises the following steps:
21) deriving a local transmission lower limit t from the imaging model constraintsiminA formula; the pixel value of the normal image should be 0,255]Within the range. The imaging model can be expressed as
Wherein, λ is the number of channels;
to restore the channel value of the image at i;
is the channel value at i of the pre-image;
is the atmospheric light value at i; t is tiIs the transmission map value at i.
The imaging model is therefore constrained by:
thereby deriving a transmission map lower bound timinIs composed of
22) Taking into account blocking artifacts and noise, the weighted transmission map estimation equation is used as follows. Performing non-coverage partition on the whole image to locally transmit the lower limit timinBased on this, the local transfer diagram t is estimated, following the assumption that the local transfer diagram is constant, by adding the tuning parametersi(ii) a Partial transmission map tiThe estimated formula of (c) is as follows:
wherein, tiIs a partial transmission diagram, timinFor the lower limit of the partial transmission map, θIs that the independent variable is timinA certain function of (a); for example,
3) parameter modification, specifically comprising the following steps:
31) obtaining the maximum channel W of the original imagei；
32) With WiFor guiding, correction of local atmospheric light
Filtering is carried out;
33) with WiFor guidance, estimate t is made for the transmission mapiFiltering is carried out;
due to atmospheric light estimation
And transmission map estimation value tiThe method is obtained by partitioning under an assumed condition, the partition edge is not smooth, and the blocking effect is obvious; with WiFor guidance, the edges of each partition can be smoothed by filtering processing, so that the image change trend is more accurate.
4) Setting an illumination value to reconstruct a tunnel image, specifically comprising the following steps:
41) carrying out simulation analysis on a large amount of grasped tunnel images to determine the average illumination value L of the images to replace Li(ii) a If the illumination value L is determined statisticallyiThe light source area is determined, the light source intensity is counted, and the estimated transmission graph t is usediCalculating L point by pointi(ii) a The calculation amount is large and time is consumed.
Simulation experiment analysis shows that: for the road tunnel image, a constant value L is taken to replace LiAnd the reconstructed image can achieve good effect.
42) Correcting the corrected local atmospheric light
Transmission map value tiSet illumination map value L and original imageImage value
Reconstructing tunnel images by substituting imaging models
To make the reconstructed image smoother, W can be usediFor guidance, the reconstructed image is filtered.
Finally, the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all of them should be covered in the claims of the present invention.
Claims (5)
1. A single tunnel image rapid enhancement method based on imaging model constraint is characterized in that: the method comprises the following steps:
1) the atmospheric light estimation specifically comprises the following steps:
11) optimizing the whole image by using a quadtree method to determine the global atmospheric light Aglob；
12) Performing uncovered partition on the whole image, and estimating local atmospheric light by using the assumption that the local atmospheric light is a constant
13) Global atmospheric light AglobAnd estimating local atmospheric light
Combined correction to obtain local atmospheric light correction value
2) The transmission map estimation specifically comprises the following steps:
21) according to the imaging modelConstraining, deriving local transmission map lower bound timinA formula;
22) performing non-covering partition on the whole image to locally transmit the lower limit t of the imageiminBased on this, the local transfer diagram t is estimated, following the assumption that the local transfer diagram is constant, by adding the tuning parametersi；
3) Parameter modification, specifically comprising the following steps:
31) obtaining the maximum channel W of the original imagei；
32) With WiFor guiding, correction of local atmospheric light
Filtering is carried out;
33) with WiFor guidance, estimate t is made for the transmission mapiFiltering is carried out;
4) setting an illumination value to reconstruct a tunnel image, specifically comprising the following steps:
41) simulating and analyzing a plurality of grasped tunnel images to determine an average illumination value L of the images to replace an illumination map Li；
42) Correcting the corrected local atmospheric light
Transmission map value tiSet illumination map value L and original image value
Reconstructing tunnel images by substituting imaging models
2. The imaging model constraint-based single tunnel image fast enhancement method as claimed in claim 1, characterized in that: in the step 11), the gray level image of the original image is divided into four parts, and the maximum average brightness is obtained; and then, quartering the area where the maximum is located, and repeating the optimization till 4-5 cycles, wherein the average brightness of the final area is theGlobal atmospheric light estimate aglob。
3. The imaging model constraint-based single tunnel image fast enhancement method as claimed in claim 2, characterized in that: in said step 12), the local atmospheric light is estimated by the following formula
Wherein D is a local pixel set; (r)i,gi,bi) Representing points on the corresponding color space; (r)l,gl,bl) Representing the image light source color point.
4. The imaging model constraint-based single tunnel image fast enhancement method of claim 3, characterized in that: in the step 13), the local atmospheric light is corrected by combining the following formula:
wherein,
as a combination coefficient, i.e.
5. The imaging model constraint-based single tunnel image fast enhancement method of any one of claims 1-4, characterized by: in said step 22), the partial transmission map tiThe estimated formula of (c) is as follows:
wherein, tiIs a partial transmission diagram, timinFor the lower limit of the partial transmission map, θ is an independent variable timinAs a function of (c).
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