CN119445050A - A method and device for enhancing image of power drone inspection with adaptive light intensity adjustment - Google Patents
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Abstract
The invention provides a method and a device for enhancing an inspection image of an electric unmanned aerial vehicle with illumination brightness self-adaptive adjustment, wherein the method comprises the steps of carrying out super-pixel segmentation on an acquired inspection image to obtain N super-pixel areas; the method comprises the steps of carrying out intrinsic image decomposition on a super-pixel region to obtain spectral reflection components of the super-pixel region, combining the spectral reflection components of each super-pixel region to obtain an image primary enhancement result, adopting a clear channel feature extraction model to process a patrol image to obtain a bright significance map, and carrying out decision fusion on the image primary enhancement result and the bright significance map to obtain a final illumination brightness enhanced recovery image. The invention can ensure that the electric unmanned aerial vehicle can acquire high-quality inspection images even under extreme illumination conditions (such as backlight and weak light), improves the intelligent operation and inspection safety and reliability of electric power facilities, can be widely applied to the fields of inspection of the electric unmanned aerial vehicle, and has wide application prospect and market value.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to an electric unmanned aerial vehicle inspection image enhancement method and device capable of adaptively adjusting illumination brightness.
Background
The electric power unmanned aerial vehicle plays an important role in the electric power overhead line inspection task by virtue of the operation convenience and the high-efficiency operation capability. Especially in places (such as mountain areas) which are difficult to reach such as complicated topography, unmanned aerial vehicle can effectively cross natural obstacle, realizes the comprehensive control to transmission line. However, in severe illumination conditions, such as backlight or insufficient light, the quality of the inspection image photographed by the unmanned aerial vehicle may be reduced, so that key features in the image are blurred and difficult to identify.
At this time, the application of the image enhancement technique is particularly important. The technology can obviously improve the image quality and make details in the image clearer and more discernable. By adopting the image enhancement technology, the electric unmanned aerial vehicle can provide accurate and reliable inspection information even under unfavorable illumination conditions, and ensures safe and stable operation and effective maintenance of electric facilities.
In view of the foregoing, there is an urgent need for a method and apparatus for enhancing inspection images of an electric unmanned aerial vehicle with adaptive adjustment of illumination brightness, so as to solve the problems in the prior art.
Disclosure of Invention
The invention aims to provide an electric unmanned aerial vehicle inspection image enhancement method with self-adaptive adjustment of illumination brightness, which aims to inhibit interference of backlight and weak light phenomena on high-quality inspection image imaging and meets the high-quality imaging requirement on the electric inspection image, and the specific technical scheme is as follows:
An electric unmanned aerial vehicle inspection image enhancement method with illumination brightness self-adaptive adjustment comprises the following steps:
performing super-pixel segmentation on the acquired inspection image Y to obtain N super-pixel areas;
The spectral reflection components of each super pixel region are combined to obtain an image primary enhancement result ;
Processing the inspection image Y by adopting a clear channel feature extraction model to obtain a bright significance map;
Preliminary enhancement of image resultsAnd a bright saliency mapDecision fusion is carried out to obtain a restored image with enhanced final illumination brightness。
In the above technical solution, preferably, the inspection image Y is represented by a super-pixel area as follows:
(1),
Wherein N is the number of super pixel areas, Represent the firstAnd super pixel areas.
In the above technical solution, it is preferable that the super pixel region is mapped according to formula (3)Performing intrinsic image decomposition:
(3),
Wherein, An nth pixel representing an ith super pixel area in the inspection image Y,Representing the spectral reflectance component of the nth pixel of the ith superpixel region,The luminance component of the nth pixel of the ith super pixel area is represented.
In the above technical solution, it is preferable that the method of formula (3) is performed according to formula (4)AndAnd (3) carrying out solving:
(4),
Wherein, Representation solutionAndIs a target function of (2); Representing the pixel c as the center and the size as A local window of the individual pixels is provided,Parameters for controlling the size of the local window; Representing the similarity of the spectral angles between pixel c and pixel v;
Obtaining a super pixel region according to the formula (3) and the formula (4) Spectral reflection components of each pixel in the array are finally obtained to obtain the whole super-pixel areaIs a spectral reflectance component of (2)。
In the above technical solution, preferably, the spectral reflection components of each super pixel region are combined to generate the image preliminary enhancement result:
(6),
Wherein, The spectral reflection component of the ith super pixel area is represented, and N is the number of super pixel areas.
In the above technical scheme, the bright significance map is preferably obtainedThe specific mode of (a) is as follows:
Extracting clear channel characteristics from the inspection image Y through a clear channel characteristic extraction model Channel characteristics are clarifiedAverage fusion is carried out to obtain a mapFor the atlasFourier transform to generate amplitude spectrumFor amplitude spectrumLog calculation to generate log spectrumAnd based on logarithmic spectrumGenerating an average spectrumFinally according to the logarithmic spectrumAnd average spectrumObtaining a bright saliency map of the dark-removed component。
In the above technical solution, preferably, the clear channel feature extraction model is:
(7),
Channel characteristics of contrast Average fusion is carried out to obtain a mapThe method comprises the following steps:
(8),
For the atlas Fourier transform to generate amplitude spectrumThe method comprises the following steps:
(9),
Based on log-spectrum Generating an average spectrumThe method comprises the following steps:
(10),
From log spectra And average spectrumObtaining a bright saliency map of the dark-removed componentThe method comprises the following steps:
(12),
Wherein, In order to patrol the red channel of the image,For the green channel of the inspection image,In order to patrol the blue channel of the image,To examine the HUE component of the image in HUE-saturation-intensity variations,To patrol the image for the saturation component in HUE-saturation-intensity variations,For the intensity component of the inspection image in HUE-saturation-intensity variations, p is the number of channels of the bright channel feature M,Representation pairA function of the fourier transform is performed,For a local kxk mean filter feature extractor, k represents the parameters of the mean filter feature extractor,Is an inverse fourier transform operation.
In the above technical solution, preferably, the local kxk mean value filtering feature extractorExpressed as:
(11)。
in the above technical solution, it is preferable that the image is preliminarily enhanced according to formula (13) And a bright saliency mapDecision fusion is carried out to obtain a restored image with enhanced final illumination brightness:
(13)。
The invention also provides an electric unmanned aerial vehicle inspection image enhancement device with the self-adaptive adjustment of the illumination brightness, which comprises a memory and a processor, wherein the processor executes the electric unmanned aerial vehicle inspection image enhancement method with the self-adaptive adjustment of the illumination brightness when running a computer instruction stored in the memory.
The technical scheme of the invention has the following beneficial effects:
The method comprises the steps of firstly, fully mining spatial structure similarity information of an image and intrinsic image spectral reflection information through a super-pixel segmentation and intrinsic image decomposition algorithm to obtain an image primary enhancement result, secondly, mining saliency information of image brightness to generate a bright saliency map, finally, carrying out decision fusion on the image primary enhancement result and the bright saliency map, and jointly mining the spatial structure similarity information of the image, the intrinsic image spectral reflection information and the saliency information of the image brightness to further improve the image enhancement effect.
The invention can ensure that the electric unmanned aerial vehicle can acquire high-quality inspection images even under extreme illumination conditions (such as backlight and weak light), improves the intelligent operation and inspection safety and reliability of electric power facilities, can be widely applied to the fields of inspection of the electric unmanned aerial vehicle, and has wide application prospect and market value.
In addition to the objects, features and advantages described above, the present invention has other objects, features and advantages. The present invention will be described in further detail with reference to the drawings.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application. In the drawings:
FIG. 1 is a flow chart of an electric unmanned aerial vehicle inspection image enhancement method with adaptive adjustment of illumination brightness;
FIG. 2a is a graph of imaging effects of a first inspection scenario without the enhancement method of the present invention;
FIG. 2b is a graph of imaging effects of a first inspection scenario employing the enhancement method of the present invention;
FIG. 3a is a graph of imaging effects of a second inspection scenario without the enhancement method of the present invention;
fig. 3b is an image effect diagram of a second inspection scene using the enhancement method of the present invention.
Detailed Description
The present invention will be described more fully hereinafter in order to facilitate an understanding of the present invention, and preferred embodiments of the present invention are set forth. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
Examples:
Referring to fig. 1, the embodiment provides a method for enhancing an inspection image of an electric unmanned aerial vehicle with adaptive adjustment of illumination brightness, which comprises the following steps:
Step S1, carrying out super-pixel segmentation on an acquired inspection image to obtain N super-pixel areas;
Preferably, in step S1, the electric power unmanned aerial vehicle is used to photograph the electric power equipment, so as to obtain an original visible light image Y (i.e. a patrol image Y) in jpg format. Specifically, the super-pixel region is a homogeneous region in the inspection image, and pixels in the homogeneous region have similar spatial information. In the embodiment, an entropy rate super-pixel segmentation technology (ERS) is adopted to obtain a super-pixel region in a patrol image, a principal component analysis algorithm is adopted to process the patrol image Y to reserve a first principal component, then graph optimization problem modeling is carried out on the first principal component, and an ERS algorithm is adopted to generate a series of super-pixel edges so as to obtain a plurality of homogeneous regions (namely super-pixel regions).
The entropy rate super pixel segmentation (ERS) is shown in the prior art "Entropy rate superpixel segmentation", Liu M, Tuzel O, Ramalingam S, et al, Proc of Processing IEEE Confense Computer Vision Pattern Recognition, 2011, 2097–2104; and the principal component analysis algorithm is shown in the prior art "Two-stage image denoising by principal component analysis with local pixel grouping", Zhang L, Dong W, Zhang D, et al, Pattern Recognition, 2010, 43(4):1531–1549;, and these two techniques are not further described in this embodiment.
Specifically, the inspection image Y is represented by a super-pixel area as:
(1),
Wherein N is the number of super pixel areas, Represent the firstAnd super pixel areas.
S2, carrying out intrinsic image decomposition on the super-pixel areas to obtain spectral reflection components of the super-pixel areas, combining the spectral reflection components of the super-pixel areas to obtain an image primary enhancement result;
Specifically, the inspection image Y can be decomposed into a spectral reflectance component R and a luminance component S by the intrinsic image, and this process is expressed as:
(2),
Wherein: First representing inspection image A number of pixels of the pixel array are arranged,Representing the first of the inspection imagesThe spectral reflection component of each pixel,Representing the first of the inspection imagesA luminance component of each pixel;
Further, wherein ,,;Respectively represent the first inspection image YThe elements of the individual pixels on the red, green and blue channels,Respectively represent the first of the inspection imagesThe spectral reflection component of each pixel is an element on the red, green and blue channels,Respectively represent the first of the inspection imagesThe luminance component of each pixel is an element on three channels of red, green and blue, r represents the red channel, g represents the green channel, and b represents the blue channel.
Furthermore, because the pixel value of the brightness component of the image is directly related to factors such as scene illumination, shadow and the like and the spectral reflection component is directly related to the material of the object, the brightness component in the image is removed, only the spectral reflection component in the image is reserved, and the interference of extreme illumination in the image can be well restrained. Thus, for super pixel regionThe intrinsic image decomposition is performed as:
(3),
Wherein, An nth pixel representing an ith super pixel area in the inspection image Y,Representing the spectral reflectance component of the nth pixel of the ith superpixel region,The luminance component of the nth pixel of the ith super pixel area is represented.
Further, in formula (3)AndThe method can be obtained by solving the following optimization equation:
(4),
Wherein, Representation solutionAndIs a target function of (2); Representing the pixel c as the center and the size as A local window of the individual pixels is provided,Parameters for controlling the size of the local window; The similarity of the spectral angles between pixel c and pixel v is indicated.
Obtaining a super pixel region according to the formula (3) and the formula (4)Spectral reflection component of each pixel in (B)Finally, the whole super pixel area is obtainedIs a spectral reflectance component of (2):
(5),
Wherein sup represents the super pixel regionThe number of inner pixels.
Combining spectral reflectance components of each super-pixel region to generate an image preliminary enhancement result:
(6),
Wherein, The spectral reflection component of the ith super pixel area is represented, and N is the number of super pixel areas.
S3, processing the inspection image by adopting a clear channel feature extraction model to obtain a bright significance map;
Specifically, the clear channel feature extraction model constructed in this embodiment is:
(7),
Wherein, In order to be able to characterize the channel,In order to patrol the red channel of the image,For the green channel of the inspection image,In order to patrol the blue channel of the image,To examine the HUE component of the image in HUE-saturation-intensity variations,To patrol the image for the saturation component in HUE-saturation-intensity variations,To examine the intensity component of the image in HUE-saturation-intensity variations.
The characteristic wave band of the maximum brightness of the inspection image is extracted to obtain the characteristic of the bright channelChannel characteristics are clarifiedAverage fusion is carried out to realize dimension reduction treatment on the characteristics, thereby obtaining a map:
(8),
Wherein p is the number of channels of the clear channel feature M.
To further mine the mapA significant map with bright image features, dark image features in the image suppressed, and a patternFourier transform to generate amplitude spectrum:
(9),
Wherein, Representation pairA function of fourier transform is performed.
Further, for amplitude spectrumLog calculation to generate log spectrumAnd generates an average spectrum according to formula (10):
(10),
Wherein, Is a local k x k means filter feature extractor.
Preferably, the local kxk means filter feature extractorThe method comprises the following steps:
(11),
Where k is a parameter of the mean filter feature extractor, in this embodiment k=3 is taken.
Further, the average spectrumCan reflect redundant dark area characteristics in an image, and then recover by a formula (12) to obtain a bright significance map of dark-removed components:
(12),
Wherein, Is an inverse fourier transform operation.
Step S4, the image is initially enhancedAnd a bright saliency mapDecision fusion is carried out to obtain a restored image with enhanced final illumination brightness。
Specifically, the image is initially enhanced according to equation (13)And a bright saliency mapDecision fusion is carried out, and interference of backlight and weak light phenomena in the image on high-quality inspection image imaging is inhibited:
(13)。
Referring to fig. 2a, fig. 2b, fig. 3a and fig. 3b, wherein fig. 2a and fig. 2b are a set of identical inspection scenes, it can be seen from fig. 2a and fig. 2b that the image is overall dark when the enhancing method of the present embodiment is not adopted, the features in the image are blurred and difficult to identify, the image is overall bright when the enhancing method of the present embodiment is adopted, the features in the image are clearer and easier to identify in comparison, and fig. 3a and fig. 3b are a set of identical inspection scenes, it can be seen from fig. 3a and fig. 3b that the image is overall dark, the features on the lower right side are blurred, the smoke in the image is difficult to identify when the enhancing method of the present embodiment is not adopted, the image is brighter and the features on the lower right side are clearer and easier to identify, and the smoke on the lower right side of the image can be clearly seen. Therefore, the enhancement method of the embodiment can effectively enhance the illumination brightness in the image, even under extreme illumination conditions (such as backlight and weak light), the electric unmanned aerial vehicle can acquire high-quality inspection images, the intelligent operation and inspection safety and reliability of the electric power facility are improved, the enhancement method can be widely applied to the fields of inspection of the electric unmanned aerial vehicle and the like, and the enhancement method has wide application prospects and market values.
The embodiment also provides an electric unmanned aerial vehicle inspection image enhancement device with self-adaptive adjustment of illumination brightness, which comprises a memory and a processor, wherein the processor executes the electric unmanned aerial vehicle inspection image enhancement method with self-adaptive adjustment of illumination brightness when running a computer instruction stored in the memory so as to process an inspection image.
The embodiment also provides another power unmanned aerial vehicle inspection image enhancement device with self-adaptive adjustment of illumination brightness, which comprises an image acquisition unit and a data calculation processing unit, wherein the image acquisition unit is used for shooting power equipment to acquire an inspection image, and the data calculation processing unit processes the acquired inspection image according to the power unmanned aerial vehicle inspection image enhancement method with self-adaptive adjustment of illumination brightness.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7580586B1 (en) * | 2005-04-19 | 2009-08-25 | Lockheed Martin Corporation | Enhanced recovery of low spatial frequency spectral information in a Fizeau Fourier transform spectrometer |
| CN103903229A (en) * | 2014-03-13 | 2014-07-02 | 中安消技术有限公司 | Night image enhancement method and device |
| US20220044375A1 (en) * | 2019-12-17 | 2022-02-10 | Dalian University Of Technology | Saliency Map Enhancement-Based Infrared and Visible Light Fusion Method |
| CN117274079A (en) * | 2023-09-05 | 2023-12-22 | 西南交通大学 | Low-brightness image enhancement system and method thereof |
| CN118212150A (en) * | 2024-02-27 | 2024-06-18 | 智洋创新科技股份有限公司 | Image enhancement method, system and storage medium for power transmission channel |
| CN118864266A (en) * | 2024-06-28 | 2024-10-29 | 北京航空航天大学 | Lightweight fusion method of infrared and visible light images based on spatial and frequency domain information |
-
2025
- 2025-01-10 CN CN202510040432.3A patent/CN119445050B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7580586B1 (en) * | 2005-04-19 | 2009-08-25 | Lockheed Martin Corporation | Enhanced recovery of low spatial frequency spectral information in a Fizeau Fourier transform spectrometer |
| CN103903229A (en) * | 2014-03-13 | 2014-07-02 | 中安消技术有限公司 | Night image enhancement method and device |
| US20220044375A1 (en) * | 2019-12-17 | 2022-02-10 | Dalian University Of Technology | Saliency Map Enhancement-Based Infrared and Visible Light Fusion Method |
| CN117274079A (en) * | 2023-09-05 | 2023-12-22 | 西南交通大学 | Low-brightness image enhancement system and method thereof |
| CN118212150A (en) * | 2024-02-27 | 2024-06-18 | 智洋创新科技股份有限公司 | Image enhancement method, system and storage medium for power transmission channel |
| CN118864266A (en) * | 2024-06-28 | 2024-10-29 | 北京航空航天大学 | Lightweight fusion method of infrared and visible light images based on spatial and frequency domain information |
Non-Patent Citations (4)
| Title |
|---|
| JUNHAO TAN等: "Wavelet-Based Mamba with Fourier Adjustment for Low-Light Image Enhancement", 《ARXIV.ORG》, 31 October 2024 (2024-10-31), pages 1 - 16 * |
| 廖常青: ""结构保持的图像增强研究"", 《中国优秀硕士学位论文全文数据库 信息科技辑》, no. 05, 15 May 2024 (2024-05-15), pages 138 - 896 * |
| 张文杰: "雾霾图像去雾技术与应用研究", 《中国优秀硕士学位论文全文数据库 信息科技辑》, no. 05, 31 May 2024 (2024-05-31), pages 138 - 1440 * |
| 谭海曙;周富强;熊瑛;李学夔;: "基于神经网络的图像亮度和对比度自适应增强", 光电子.激光, no. 12, 15 December 2010 (2010-12-15), pages 141 - 144 * |
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