CN114119389A - Image restoration method, system and storage module - Google Patents
Image restoration method, system and storage module Download PDFInfo
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Abstract
An image restoration method, system and storage module, the image restoration method comprising: acquiring an original image and shooting information of the original image, wherein the shooting information comprises shooting positioning and shooting angles; the shooting positioning is the positioning of the camera device when shooting the original image, and the shooting angle comprises a wide angle and a pitch angle when the camera device shoots the original image; acquiring a landmark characteristic capable of calibrating a geographic coordinate from an original image, acquiring a pixel point corresponding to the landmark characteristic and marking the pixel point as a characteristic pixel point, and marking the geographic coordinate mark corresponding to the landmark characteristic as a reference coordinate; and calculating the depth of field of the characteristic pixel point by combining shooting positioning and reference coordinates to serve as the reference depth of field. According to the method and the device, the rapidness, convenience, high efficiency and accuracy of obtaining the depth of field of the characteristic pixel points are guaranteed, a foundation is laid for the accurate calculation of the depth of field of the residual pixel points on the subsequent original image, and therefore the guarantee is provided for the high-quality restoration of the original image.
Description
Technical Field
The present invention relates to the field of image processing, and in particular, to an image restoration method, system and storage module.
Background
As image processing techniques are widely applied to various industries, people are pursuing higher and higher image quality. In this case, there are many technical studies on image restoration.
The most recommended image restoration technology in the prior art is to restore an original image by combining the depth of field of a pixel point and a pre-constructed image restoration model. At present, the depth of field of a pixel point can be obtained according to two methods: one is that the distance from the shooting position to partial pixel points on the original image, namely the depth of field, is manually measured; the other is considered to estimate the distance from the shooting position to a part of the pixel points on the original image, i.e., the depth of field. The former method has high measurement difficulty, long time consumption and difficult implementation; the latter has a large error and is difficult to ensure the image restoration quality.
Disclosure of Invention
In order to solve the defect that the depth of field of a pixel point on a shot original image is difficult to acquire in the prior art, the invention provides an image restoration method, an image restoration system and a storage module.
One of the objectives of the present invention is to provide an image restoration method, which can accurately and quickly obtain the depth of field of a pixel point on an original image, and provide a guarantee for the implementation and quality of image restoration.
An image restoration method comprising the steps of:
s1, acquiring an original image and shooting information of the original image, wherein the shooting information comprises shooting positioning and shooting angles; the shooting positioning is the positioning of the camera device when shooting the original image, and the shooting angle comprises a wide angle and a pitch angle when the camera device shoots the original image;
s2, acquiring landmark characteristics capable of calibrating geographic coordinates from the original image, acquiring pixel points corresponding to the landmark characteristics and marking the pixel points as characteristic pixel points, and taking geographic coordinate marks corresponding to the landmark characteristics as reference coordinates; calculating the depth of field of the characteristic pixel point as the reference depth of field by combining shooting positioning and reference coordinates;
s3, calculating the depth of field of each pixel point on the original image by combining the shooting angle and the reference depth of field;
and S4, carrying out image restoration on the original image by combining the image restoration model with the depth of field of each pixel point on the original image.
Preferably, in S2, the reference coordinates are acquired by: acquiring image features existing on a GIS map from an original image as landmark features, and acquiring geographic coordinates corresponding to the landmark features as reference coordinates by combining the GIS map; the symbolic features include buildings and natural attractions.
Preferably, the photographing information further includes weather information, and S4 includes the following sub-steps:
s41, setting weather types, and constructing image restoration models corresponding to the weather types one by one;
s42, acquiring the weather category to which the weather information in the shooting information belongs, and acquiring an image restoration model corresponding to the weather category as a target model;
and S43, restoring the original image by combining the target model with the depth of field of each pixel point on the original image.
Preferably, the weather category includes weather conditions and time conditions, the weather conditions including: fog, rain, sunny and snowy; the time conditions include: day and night.
Preferably, in S1, the method for acquiring weather information of the original image includes the following sub-steps:
s11, acquiring shooting time and shooting positioning related to the original image;
s12, obtaining local time conditions and weather conditions by combining the shooting time and the shooting positioning networking;
and S13, generating weather information by combining local time conditions and weather conditions.
The second object of the present invention is to provide an image restoration system suitable for the image restoration method.
An image restoration system comprising: a camera and a processor;
the camera device is used for shooting an original image, and shooting information is associated with the original image; the shooting information comprises shooting positioning and shooting angles; the shooting positioning is the positioning of the camera device when shooting the original image, and the shooting angles are the wide angle and the pitch angle of the camera device when shooting the original image;
the processor is used for acquiring an original image shot by the camera device and processing the original image according to the image restoration method.
Preferably, the system further comprises a model storage module, wherein the model storage module is used for storing the image restoration models which correspond to the meteorological categories one by one.
Preferably, the system further comprises a parameter editing module, wherein the parameter editing module is used for manually setting the marking characteristics and the reference coordinates.
The storage module provided by the third object of the invention is beneficial to popularization of the image restoration method.
A storage module storing a computer program for implementing the image restoration method described above when executed.
The invention has the advantages that:
(1) according to the image restoration method provided by the invention, the depth of field of the characteristic pixel point in the original image is obtained by combining shooting positioning and the geographic coordinate of the landmark characteristic, so that the rapidness, convenience, high efficiency and accuracy of obtaining the depth of field of the characteristic pixel point are ensured, a foundation is laid for the accurate calculation of the depth of field of the residual pixel points on the subsequent original image, and thus, the guarantee is provided for the high-quality restoration of the original image.
(2) The landmark characteristics on the original image of the original image are automatically identified by combining the GIS map, and the objectivity of landmark characteristic extraction is ensured, so that the precise correspondence between the landmark characteristics and the reference coordinates is ensured.
(3) The corresponding image restoration models are established for different weather categories, so that the original image is restored pertinently according to the shooting conditions, and the quality of image restoration is further improved.
(4) The weather category comprises weather conditions and time conditions, the time conditions corresponding to the original images can be judged according to the shooting time and the shooting positioning, the time difference condition is considered, and the accurate selection of the image restoration model aiming at the shooting conditions of the original images is further ensured, so that the image restoration quality is ensured.
(5) The invention provides an image restoration system which can realize high-quality restoration of an original image shot by a shooting device. The system stores image restoration models corresponding to the meteorological categories one by one. Therefore, when the processor restores the original image, the stored image restoration model can be directly called, and the image processing efficiency is improved.
(6) The image restoration system further comprises a parameter editing module. Therefore, when the landmark characteristics or the geographic coordinates of the landmark characteristics cannot be determined, the staff can manually set the landmark characteristics and the reference coordinates through the parameter editing module, the emergency work of the system can be guaranteed, and the working reliability is improved.
(7) According to the storage medium provided by the invention, the image restoration method is written into the storage module in a computer program mode, so that plug and play are realized, and the image restoration method is favorable for popularization.
Drawings
FIG. 1 is a flow chart of a method of image restoration;
FIG. 2 is a flow chart of another image restoration method;
FIG. 3 is a flowchart of a method for obtaining weather information from an original image.
Detailed Description
Referring to fig. 1, an image restoration method according to the present embodiment includes the steps of:
s1, acquiring an original image and shooting information of the original image, wherein the shooting information comprises shooting positioning and shooting angles; the shooting positioning is the positioning when the camera device shoots the original image, and the shooting angle comprises a wide angle and a pitch angle when the camera device shoots the original image. The shooting location and the shooting angle are automatically recorded by the camera.
S2, acquiring landmark characteristics capable of calibrating geographic coordinates from the original image, acquiring pixel points corresponding to the landmark characteristics and marking the pixel points as characteristic pixel points, and taking geographic coordinate marks corresponding to the landmark characteristics as reference coordinates; and calculating the depth of field of the characteristic pixel point by combining shooting positioning and reference coordinates to serve as the reference depth of field.
S3, calculating the depth of field of each pixel point on the original image by combining the shooting angle and the reference depth of field;
and S4, carrying out image restoration on the original image by combining the image restoration model with the depth of field of each pixel point on the original image.
In step S2, the number of the feature pixels is greater than 1, and specifically, the feature pixels can be set according to the size of the actual space covered by the original image, and the larger the actual space is, the larger the number of the feature pixels is. In the embodiment, the number of the characteristic pixel points is at least 3, so that the spatial distance on the original image can be more clearly extracted, and the accuracy of the depth of field of the residual pixel points on the original image calculated by combining the shooting angle and the reference depth of field is ensured. In order to further ensure the accuracy of depth-of-field calculation, the selected multiple characteristic pixel points should have different depths of field.
It should be noted that, in step S3, in the case that the reference depth and the shooting angle are known, the depth calculation of the remaining pixels (i.e. pixels other than the characteristic pixels) in the original image can be implemented by using the prior art, and therefore, the description thereof is omitted here.
In step S4, the input of the image restoration model is the original image and the depth of field of each pixel on the original image, and the output of the image restoration model is the restored image. In particular, the image restoration model may be an image restoration model known in the art.
In specific implementation, an image restoration model can be obtained according to neural network training, and the specific steps are as follows: obtaining a training sample, wherein the training sample consists of a shot original image and the depth of field of each pixel point on the original image; and acquiring a restored image corresponding to each training sample as a label of the training sample, then learning the training sample and the corresponding label based on a neural network model to acquire an image restoration model which is input as an original image and the depth of field of each pixel point on the original image and output as a restored image.
In the embodiment, the depth of field of the feature pixel points in the original image is obtained by combining shooting positioning and the geographic coordinates of the landmark features, so that the rapidness, convenience, high efficiency and accuracy of obtaining the depth of field of the feature pixel points are ensured, a foundation is laid for the accurate calculation of the depth of field of the residual pixel points on the subsequent original image, and the guarantee is provided for the high-quality restoration of the original image.
In step S2, the reference coordinates are acquired by: acquiring image features existing on a GIS map from an original image as landmark features, and acquiring geographic coordinates corresponding to the landmark features as reference coordinates by combining the GIS map; the symbolic features include buildings and natural attractions. In this way, in the embodiment, the landmark features on the original image of the original image are automatically identified by combining the GIS map, so that the objectivity of landmark feature extraction is ensured, and accurate correspondence between the landmark features and the reference coordinates is ensured.
In specific implementation, after an original image is obtained, a shooting area can be determined by combining shooting positioning and shooting angles associated with the original image, then a model in the shooting area is amplified on a GIS map to be used as a live-action model, then image features matched with the original image and the live-action model are determined by the existing image comparison technology to be used as screening features, and the screening features capable of determining geographic coordinates are obtained by combining the live-action model to be used as landmark features, so that reference coordinates of the landmark features are obtained.
In the embodiment, the GIS map is combined to screen and position the image features in the original image, so that the accurate identification and positioning of the landmark features are ensured, and the intellectualization, accuracy and reliability of obtaining the feature pixel points by combining the reference coordinate and shooting positioning are ensured.
Referring to fig. 2, in the present embodiment, the photographing information further includes weather information, and S4 includes the following substeps.
And S41, setting weather types, and constructing image restoration models corresponding to the weather types one by one.
And S42, acquiring the weather type of the weather information in the shooting information, and acquiring an image restoration model corresponding to the weather type as a target model.
And S43, restoring the original image by combining the target model with the depth of field of each pixel point on the original image.
In specific implementation, the weather category includes weather conditions and time conditions, and the weather conditions include: fog, rain, sunny and snowy; the time conditions include: day and night. As described above, in the present embodiment, eight weather categories can be obtained by combining the weather conditions and the time conditions: foggy day, rainy day, sunny day, snowy day, foggy night, rainy night, sunny night, and snowy night. In particular embodiments, the weather conditions may be further categorized, for example, rainy days may be categorized into light rain, medium rain, heavy rain, etc., such that more detailed weather conditions result in more detailed weather categories, such as light rain day, medium rain day, heavy rain day, etc.
In the embodiment, the corresponding image restoration models are established for different weather types, so that the original image is restored specifically according to the shooting conditions, and the quality of image restoration is further improved.
In specific implementation, the weather information can be obtained according to weather forecast data. Referring to fig. 3, in step S1, the method of acquiring weather information of an original image includes the following sub-steps.
And S11, acquiring the shooting time and shooting location associated with the original image.
And S12, obtaining local time conditions and weather conditions by combining the shooting time and the shooting positioning networking.
And S13, generating weather information by combining local time conditions and weather conditions.
It should be noted that the weather conditions in step S12 may be classified in the same way as the weather conditions in the weather category corresponding to the image restoration model, for example, the weather conditions are all classified in fog days, rain days, fine days and snowy days; or the weather conditions in step S12 are divided into more specific divisions with respect to the weather conditions in the weather category corresponding to the image restoration model, for example, the weather conditions in the weather category corresponding to the image restoration model are divided into a division of fog, rain, fine and snow, and the weather conditions in step S12 are divided into a division of little fog, big fog, little rain, medium rain, heavy rain, cloudy, fine, small snow, medium snow, university and heavy snow with reference to the weather forecast. Therefore, the corresponding weather category can be accurately locked according to the weather information of the original image.
In the embodiment, the time condition corresponding to the original image can be judged according to the shooting time and the shooting positioning, the time difference condition is considered, and the accurate selection of the image restoration model aiming at the shooting condition of the original image is further ensured, so that the image restoration quality is ensured.
In this embodiment, an image restoration system includes: an imaging device and a processor.
The camera device is used for shooting an original image, and shooting information is associated with the original image; the shooting information comprises shooting positioning and shooting angles; the shooting positioning is the positioning of the camera device when shooting the original image, and the shooting angles are the wide angle and the pitch angle of the camera device when shooting the original image.
Specifically, the shooting positioning is provided by a positioning module of the camera device, and the shooting angle is provided by an angle sensor of the camera device.
The processor is used for acquiring an original image shot by the camera device and processing the original image according to the image restoration method. The processor obtains the landmark features from the original image as feature pixel points after obtaining the original image associated with the shooting information, obtains the geographic coordinates of the landmark features as reference coordinates corresponding to the feature pixel points by combining a GIS map, then calculates the distance between the shooting position and the reference coordinates as the depth of field of the feature pixels as the reference depth of field, and then obtains the depth of field of each pixel point left on the original image by combining the shooting angle and the reference depth of field. Then, the processor can combine the depth of field of each pixel point on the original image and the image restoration model to restore the original image.
The image restoration system further comprises a model storage module, wherein the model storage module is used for storing the image restoration models which correspond to the meteorological categories one by one. Therefore, when the processor restores the original image, the stored image restoration model can be directly called, and the image processing efficiency is improved.
The image restoration system further comprises a parameter editing module, wherein the parameter editing module is used for manually setting the landmark characteristics and the reference coordinates. Therefore, when the landmark characteristics or the geographic coordinates of the landmark characteristics cannot be determined, the staff can manually set the landmark characteristics and the reference coordinates through the parameter editing module, the emergency work of the system can be guaranteed, and the working reliability is improved.
The present embodiment also provides a storage module, which stores a computer program for implementing the image restoration method described above when the computer program is executed. In this way, in the present embodiment, by writing the image restoration method in the storage module in the form of a computer program, plug and play is realized, which is advantageous for the popularization of the image restoration method.
The invention is not to be considered as limited to the specific embodiments shown and described, but is to be understood to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the invention as defined by the appended claims.
Claims (9)
1. An image restoration method, comprising the steps of:
s1, acquiring an original image and shooting information of the original image, wherein the shooting information comprises shooting positioning and shooting angles; the shooting positioning is the positioning of the camera device when shooting the original image, and the shooting angle comprises a wide angle and a pitch angle when the camera device shoots the original image;
s2, acquiring landmark characteristics capable of calibrating geographic coordinates from the original image, acquiring pixel points corresponding to the landmark characteristics and marking the pixel points as characteristic pixel points, and taking geographic coordinate marks corresponding to the landmark characteristics as reference coordinates; calculating the depth of field of the characteristic pixel point as the reference depth of field by combining shooting positioning and reference coordinates;
s3, calculating the depth of field of each pixel point on the original image by combining the shooting angle and the reference depth of field;
and S4, carrying out image restoration on the original image by combining the image restoration model with the depth of field of each pixel point on the original image.
2. The image restoration method according to claim 1, wherein in S2, the reference coordinates are acquired by: acquiring image features existing on a GIS map from an original image as landmark features, and acquiring geographic coordinates corresponding to the landmark features as reference coordinates by combining the GIS map; the symbolic features include buildings and natural attractions.
3. The image restoration method according to claim 1, wherein the photographing information further includes weather information, and S4 includes the sub-steps of:
s41, setting weather types, and constructing image restoration models corresponding to the weather types one by one;
s42, acquiring the weather category to which the weather information in the shooting information belongs, and acquiring an image restoration model corresponding to the weather category as a target model;
and S43, restoring the original image by combining the target model with the depth of field of each pixel point on the original image.
4. The image restoration method according to claim 3, wherein the weather category includes weather conditions and time conditions, the weather conditions including: fog, rain, sunny and snowy; the time conditions include: day and night.
5. The image restoration method according to claim 4, wherein in S1, the method for acquiring the weather information of the original image comprises the following sub-steps:
s11, acquiring shooting time and shooting positioning related to the original image;
s12, obtaining local time conditions and weather conditions by combining the shooting time and the shooting positioning networking;
and S13, generating weather information by combining local time conditions and weather conditions.
6. An image restoration system, comprising: a camera and a processor;
the camera device is used for shooting an original image, and shooting information is associated with the original image; the shooting information comprises shooting positioning and shooting angles; the shooting positioning is the positioning of the camera device when shooting the original image, and the shooting angles are the wide angle and the pitch angle of the camera device when shooting the original image;
the processor is used for acquiring an original image shot by the camera device, and is also used for processing the original image according to the image restoration method of any one of the above claims 1 to 5.
7. The image restoration system according to claim 6, further comprising a model storage module for storing image restoration models corresponding to the weather categories one to one.
8. The image restoration system according to claim 6, further comprising a parameter editing module for manually setting the landmark features and the reference coordinates.
9. A storage module storing a computer program for implementing the image restoration method according to any one of claims 1 to 5 when the computer program is executed.
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| CN116758150A (en) * | 2023-05-15 | 2023-09-15 | 阿里云计算有限公司 | Position information determining method and device |
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| CN116758150A (en) * | 2023-05-15 | 2023-09-15 | 阿里云计算有限公司 | Position information determining method and device |
| CN116758150B (en) * | 2023-05-15 | 2024-04-30 | 阿里云计算有限公司 | Position information determining method and device |
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