CN114119411A - Fog noise video image recovery method, device, equipment and medium - Google Patents
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Abstract
The invention relates to the technical field of image/video processing, and discloses a fog noise video image recovery method, a device, equipment and a medium, wherein a foggy image is obtained, and a dark channel image of the foggy image is obtained; calculating an atmospheric light value of the foggy image according to the dark channel image; estimating the transmittance of the image by using a non-local prior and the atmospheric light value; thinning the transmissivity by adopting guide filtering to obtain the treated transmissivity; restoring the foggy image into a fogless image according to the processed transmittance and the atmospheric light value; and optimizing the fog-free image through a single-scale Retinex algorithm to output a defogged image, thereby realizing the defogging effect of the video image with the fog noise.
Description
Technical Field
The present application relates to the field of image/video processing technologies, and in particular, to a method, an apparatus, a device, and a medium for restoring a fog noise video image.
Background
In real life, fog and haze formed by substances such as smoke and dust floating in the air cause deterioration of image quality, and these images are called degraded images. The contrast of the degraded image is reduced, and some contents are even blurred, which causes adverse effects on outdoor photographing and computer vision application, and the remote sensing image is easily affected by severe weather, so that the problems of low image saturation, color distortion, blurred details and the like are caused. Directly influences the subsequent work of visual interpretation, spectral analysis, feature extraction and the like of the image. The defogging processing is carried out on the degraded image, the image quality is improved, the visual effect of the image is improved, and the defogging processing method has very important practical significance in the fields of image processing and computer vision application. At present, the algorithms for processing the fog images clearly are mainly divided into two categories: image enhancement based methods and physical model based methods.
The image enhancement method generally adopts gamma correction, histogram equalization and other methods to achieve the aim of defogging by improving the contrast of an image. The method based on image enhancement is simple in calculation, but easily causes loss of image information. Therefore, how to realize fog noise video image restoration becomes a technical problem to be solved urgently.
The above is only for the purpose of assisting understanding of the technical aspects of the present invention, and does not represent an admission that the above is prior art.
Disclosure of Invention
The invention mainly aims to provide a fog noise video image recovery method, a fog noise video image recovery device, fog noise video image recovery equipment and a fog noise video image recovery medium, and aims to solve the technical problem that the fog noise video image recovery cannot be realized in the prior art.
In order to achieve the above object, the present invention provides a fog noise video image restoration method, including:
acquiring a foggy image, and solving a dark channel image of the foggy image;
calculating an atmospheric light value of the foggy image according to the dark channel image;
estimating the transmittance of the image by using a non-local prior and the atmospheric light value;
thinning the transmissivity by adopting guide filtering to obtain the treated transmissivity;
restoring the foggy image into a fogless image according to the processed transmittance and the atmospheric light value;
and optimizing the haze-free image through a single-scale Retinex algorithm to output a haze-removed image.
Optionally, the step of acquiring the foggy image and obtaining the dark channel image of the foggy image includes:
obtaining a foggy image J, by formula
Obtaining a dark channel image J corresponding to the foggy image JdarkWherein JcA certain color channel representing J; Ω (x) is a filtering window centered around pixel point x.
Optionally, the step of calculating the atmospheric light value of the foggy image according to the dark channel image includes:
and according to the positions of pixel points 0.1% before the pixel values in the dark channel image, then solving the average value of the pixel values of the corresponding positions in the foggy image as the atmospheric light value of the image.
Optionally, the step of estimating the transmittance of the image using a non-local prior and the atmospheric light value comprises:
estimating the image transmittance by using a non-local prior algorithm and the atmospheric light value A,
where ω is the retention coefficient of the mist.
Optionally, the step of restoring the foggy image into a fogless image according to the processed transmittance and the atmospheric light value includes:
restoring the foggy image according to the processed transmittance and the atmospheric light value by using an atmospheric scattering model according to the following formula:
wherein I (x) is a hazy image; j (x) is a fog-free image; t (x) is the transmittance, describing the proportion of the object's reflected light that can pass through attenuation to reach the observation point; and a is atmospheric light.
Optionally, the step of performing optimization processing on the haze-free image through a single-scale Retinex algorithm to output a haze-removed image includes:
calculating a brightness image of the fog-free image through a single-scale Retinex algorithm;
estimating illumination components and solving reflection components;
smoothing the obtained brightness image by adopting guide filtering, estimating an illumination component, and then obtaining a reflection component in a logarithmic domain;
carrying out S-shaped function enhancement, global adaptive contrast adjustment and Gamma brightness adjustment on the reflection component;
wherein, Gamma conversion is adopted to adjust the brightness of the reflection component, and the expression of the Gamma conversion is as follows:
S＝cγγ
the contrast of the image can be well stretched, the gray level can be expanded, and different effects can be achieved by setting different parameters. Setting gamma <1, the image can be brightened; the gamma is set to be more than 1, so that the image can be darkened, the contrast of the image is improved, and the details are highlighted;
and calculating each channel by using a color recovery function, integrating the three channels and outputting a defogged image.
Optionally, the step of performing S-type function enhancement, global adaptive contrast adjustment, and Gamma brightness adjustment on the reflection component includes:
a large amount of image detail information exists in the reflection component, so in order to obtain the image detail information, the reflection component needs to be further enhanced;
the formula of the sigmoid function is defined as:
wherein, omega is the calculated reflection component, and because omega is in the log domain, the value range of omega is set as [ -1,1 ]; a is a parameter.
Further, to achieve the above object, the present invention also proposes a fog noise video image restoration apparatus, comprising:
the image acquisition module is used for acquiring a foggy image and solving a dark channel image of the foggy image;
the light value calculation module is used for calculating the atmospheric light value of the foggy image according to the dark channel image;
the estimation calculation module is used for adopting non-local prior and the atmospheric light value to estimate the transmissivity of the image;
the calculation refinement module is used for performing refinement processing on the transmissivity by adopting guide filtering to obtain the processed transmissivity;
the image restoration module is used for restoring the foggy image into a fogless image according to the processed transmissivity and the atmospheric light value;
and the optimization output module is used for optimizing the haze-free image through a single-scale Retinex algorithm so as to output a defogged image.
In addition, to achieve the above object, the present invention also provides a computer device, including: a memory, a processor and a fog noise video image restoration program stored on said memory and executable on said processor, said fog noise video image restoration program being configured to implement a fog noise video image restoration method as described above.
Furthermore, to achieve the above object, the present invention also proposes a medium having stored thereon a fog noise video image restoration program which, when executed by a processor, implements the steps of the fog noise video image restoration method as described above.
The method comprises the steps of obtaining a foggy image and obtaining a dark channel image of the foggy image; calculating an atmospheric light value of the foggy image according to the dark channel image; estimating the transmittance of the image by using a non-local prior and the atmospheric light value; thinning the transmissivity by adopting guide filtering to obtain the treated transmissivity; restoring the foggy image into a fogless image according to the processed transmittance and the atmospheric light value; and optimizing the fog-free image through a single-scale Retinex algorithm to output a defogged image, thereby realizing the defogging effect of the video image with the fog noise.
Drawings
Fig. 1 is a schematic structural diagram of a fog noise video image restoration device in a hardware operating environment according to an embodiment of the present invention;
fig. 2 is a flowchart illustrating a fog noise video image restoration method according to a first embodiment of the present invention.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a fog noise video image restoration apparatus in a hardware operating environment according to an embodiment of the present invention.
As shown in fig. 1, the fog noise video image restoration apparatus may include: a processor 1001, such as a Central Processing Unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a Wireless interface (e.g., a Wireless-Fidelity (WI-FI) interface). The Memory 1005 may be a Random Access Memory (RAM) Memory, or may be a Non-Volatile Memory (NVM), such as a disk Memory. The memory 1005 may alternatively be a storage device separate from the processor 1001.
It will be appreciated by those skilled in the art that the arrangement shown in figure 1 does not constitute a limitation of the fog noise video image restoration apparatus and may include more or fewer components than shown, or some components in combination, or a different arrangement of components.
As shown in fig. 1, a memory 1005, which is a storage medium, may include therein an operating system, a data storage module, a network communication module, a user interface module, and a fog noise video image restoration program.
In the fog noise video image restoration apparatus shown in fig. 1, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the fog noise video image restoration device of the present invention may be provided in a fog noise video image restoration device that calls a fog noise video image restoration program stored in the memory 1005 through the processor 1001 and executes the fog noise video image restoration method provided by the embodiment of the present invention.
An embodiment of the present invention provides a fog noise video image recovery method, and referring to fig. 2, fig. 2 is a schematic flow diagram of a first embodiment of the fog noise video image recovery method according to the present invention.
In this embodiment, the fog noise video image restoration method includes the following steps:
step S10: and acquiring a foggy image, and solving a dark channel image of the foggy image.
It should be noted that the dark channel image, the so-called dark channel, is a basic assumption, which considers that in most non-sky local areas, at least one color channel of some pixels has a very low value. It is easy to understand that the reason for this assumption in real life is many, such as the shadow in a car, building or city, or bright objects or surfaces (such as green leaves, various bright flowers, or blue-green sleep), darker objects or surfaces, and the dark path of these objects is always changed to a darker state.
Further, the step of acquiring the foggy image and obtaining the dark channel image of the foggy image includes:
obtaining a foggy image J, by formula
Obtaining a dark channel image J corresponding to the foggy image JdarkWherein JcA certain color channel representing J; Ω (x) is a filtering window centered around pixel point x.
Step S20: calculating an atmospheric light value of the foggy image according to the dark channel image;
further, the step of calculating the atmospheric light value of the foggy image according to the dark channel image comprises: and according to the positions of pixel points 0.1% before the pixel values in the dark channel image, then solving the average value of the pixel values of the corresponding positions in the foggy image as the atmospheric light value A of the image.
Step S30: estimating the transmittance of the image by using a non-local prior and the atmospheric light value;
further, the step of estimating the transmittance of the image using the non-local prior and the atmospheric light value includes:
estimating the image transmittance by using a non-local prior algorithm and the atmospheric light value A,
where ω is the retention coefficient of the mist.
Step S40: thinning the transmissivity by adopting guide filtering to obtain the treated transmissivity;
it should be noted that the Guided filter (Guided filter) explicitly calculates the output image using a guide image, where the guide image may be the input image itself or another image. The guiding filtering has better effect near the boundary than the bilateral filtering; in addition, it has the speed advantage of linear time of O (N).
In the specific implementation, the edge position of an object in the image obtained by performing image defogging processing on the roughly estimated transmittance has an obvious fuzzy region, and in order to eliminate the influence of the undesirable phenomenon on the defogging effect, the defogging transmittance estimation link needs to be optimized. And thinning the transmissivity by adopting guide filtering.
Step S50: restoring the foggy image into a fogless image according to the processed transmittance and the atmospheric light value;
further, the restoring the foggy image to a fogless image according to the processed transmittance and the atmospheric light value includes: restoring the foggy image according to the processed transmittance and the atmospheric light value by using an atmospheric scattering model according to the following formula:
wherein I (x) is a hazy image; j (x) is a fog-free image; t (x) is the transmittance, describing the proportion of the object's reflected light that can pass through attenuation to reach the observation point; and a is atmospheric light.
Step S60: and optimizing the haze-free image through a single-scale Retinex algorithm to output a haze-removed image.
In specific implementation, the defogged image obtained by the dark channel defogging algorithm can theoretically show a good defogging effect, and in order to further enhance the color contrast and detail expression of the defogged image, the single-scale Retinex algorithm is selected for the post-optimization processing of the defogged image. The basic model of the single-scale Retinex algorithm (SSR) is as follows:
where S (x, y) is the original image, R (x, y) is the reflection component, L (x, y) is the illumination component, R is the reflection componentiA reflection image representing the ith color channel, the convolution operator being represented by x, and the center surround function being represented by F (x, y); and the variable c is a scale parameter and is used for controlling the integral filtering strength of the filter. To show dimensions of different sizesThe influence of the parameters on the filtering strength of the gaussian function.
In a specific implementation, a luminance image of the preliminary restoration image is calculated. Firstly, a brightness image of a preliminary restored image is obtained by adopting a weighted average method and is used as an initial image of the algorithm.
The illumination component is estimated, and the reflection component is obtained. The luminance image thus obtained is smoothed by using guided filtering, the illumination component is estimated, and the reflection component is obtained in the logarithmic domain.
And performing a series of operations such as S-shaped function enhancement, global adaptive contrast adjustment, Gamma brightness adjustment and the like on the reflection component.
There is a lot of image detail information in the reflection component, so to obtain the image detail information, the reflection component needs to be further enhanced.
The formula of the sigmoid function is defined as:
wherein, ω is the reflection component that is solved, because ω is in the log domain, so ω may take the negative value, the value range of this text setting ω is [ -1,1 ]; a is a parameter, and the values of a are different, so that the enhancement effect is different. The enhancement effect is best when the a is 6.
Wherein, Gamma conversion is adopted to adjust the brightness of the reflection component, and the expression of the Gamma conversion is as follows:
S＝cγγ
the contrast of the image can be well stretched, the gray level can be expanded, and different effects can be achieved by setting different parameters. Setting gamma <1, the image can be brightened; the gamma is set to be more than 1, so that the image can be darkened, the contrast of the image is improved, and the details are highlighted.
A color recovery function; first, a is obtained as max (I)R[i],IG[i],IB[i]) Then, the amplification factor is found:
where R' is the input image and Int is the luminance image.
Next, each channel is calculated
R＝M IR[i]
G＝M IG[i]
B＝M IB[i]
And finally, integrating the 3 channels to obtain the recovered defogged clear image.
Further, the step of performing optimization processing on the haze-free image through a single-scale Retinex algorithm to output a haze-removed image includes: calculating a brightness image of the fog-free image through a single-scale Retinex algorithm; estimating illumination components and solving reflection components; carrying out S-shaped function enhancement, global adaptive contrast adjustment and Gamma brightness adjustment on the reflection component; each channel is calculated using a color recovery function to output a defogged image.
Further, the step of performing sigmoid function enhancement, global adaptive contrast adjustment, and Gamma brightness adjustment on the reflection component includes: a large amount of image detail information exists in the reflection component, so in order to obtain the image detail information, the reflection component needs to be further enhanced;
the formula of the sigmoid function is defined as:
wherein, omega is the calculated reflection component, and because omega is in the log domain, the value range of omega is set as [ -1,1 ]; a is a parameter.
The embodiment acquires a foggy image and obtains a dark channel image of the foggy image; calculating an atmospheric light value of the foggy image according to the dark channel image; estimating the transmittance of the image by using a non-local prior and the atmospheric light value; thinning the transmissivity by adopting guide filtering to obtain the treated transmissivity; restoring the foggy image into a fogless image according to the processed transmittance and the atmospheric light value; and optimizing the fog-free image through a single-scale Retinex algorithm to output a defogged image, thereby realizing the defogging effect of the video image with the fog noise.
Furthermore, an embodiment of the present invention further provides a medium, on which a fog noise video image restoration program is stored, where the fog noise video image restoration program, when executed by a processor, implements the steps of the fog noise video image restoration method as described above.
The embodiments or specific implementation manners of the fog noise video image restoration apparatus according to the present invention may refer to the above-mentioned method embodiments, and are not described herein again.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., a rom/ram, a magnetic disk, an optical disk) and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A fog noise video image restoration method, the method comprising:
acquiring a foggy image, and solving a dark channel image of the foggy image;
calculating an atmospheric light value of the foggy image according to the dark channel image;
estimating the transmittance of the image by using a non-local prior and the atmospheric light value;
thinning the transmissivity by adopting guide filtering to obtain the treated transmissivity;
restoring the foggy image into a fogless image according to the processed transmittance and the atmospheric light value;
and optimizing the haze-free image through a single-scale Retinex algorithm to output a haze-removed image.
2. The method of claim 1, wherein the step of acquiring the foggy image and obtaining the dark channel image of the foggy image comprises:
obtaining a foggy image J, by formula
Obtaining a dark channel image J corresponding to the foggy image JdarkWherein JcA certain color channel representing J; Ω (x) is a filtering window centered around pixel point x.
3. The method of claim 1, wherein the step of calculating the atmospheric light value of the foggy image from the dark channel image comprises:
and according to the positions of pixel points 0.1% before the pixel values in the dark channel image, then solving the average value of the pixel values of the corresponding positions in the foggy image as the atmospheric light value of the image.
4. The method of claim 1, wherein the step of estimating the transmittance of the image using the non-local prior and the atmospheric light values comprises:
by using non-local preorders
An algorithm and the atmospheric light value a estimate the image transmittance,
where ω is the retention coefficient of the mist.
5. The method of claim 1, wherein said step of restoring said hazy image to a haze-free image based on said processed transmittance and said atmospheric light value comprises:
restoring the foggy image according to the processed transmittance and the atmospheric light value by using an atmospheric scattering model according to the following formula:
wherein I (x) is a hazy image; j (x) is a fog-free image; t (x) is the transmittance, describing the proportion of the object's reflected light that can pass through attenuation to reach the observation point; and a is atmospheric light.
6. The method of claim 1, wherein the step of performing optimization processing on the haze-free image by a single-scale Retinex algorithm to output a haze-removed image comprises:
calculating a brightness image of the fog-free image through a single-scale Retinex algorithm;
estimating illumination components and solving reflection components;
smoothing the obtained brightness image by adopting guide filtering, estimating an illumination component, and then obtaining a reflection component in a logarithmic domain;
carrying out S-shaped function enhancement, global adaptive contrast adjustment and Gamma brightness adjustment on the reflection component;
wherein, Gamma conversion is adopted to adjust the brightness of the reflection component, and the expression of the Gamma conversion is as follows:
S＝cγγ
the contrast of the image can be well stretched, the gray level can be expanded, and different effects can be achieved by setting different parameters. Setting gamma <1, the image can be brightened; the gamma is set to be more than 1, so that the image can be darkened, the contrast of the image is improved, and the details are highlighted;
and calculating each channel by using a color recovery function, integrating the three channels and outputting a defogged image.
7. The method of claim 6, wherein said step of performing sigmoid function enhancement, global adaptive contrast adjustment, Gamma brightness adjustment on said reflected component comprises:
a large amount of image detail information exists in the reflection component, so in order to obtain the image detail information, the reflection component needs to be further enhanced;
the formula of the sigmoid function is defined as:
wherein, omega is the calculated reflection component, and because omega is in the log domain, the value range of omega is set as [ -1,1 ]; a is a parameter.
8. A fog noise video image restoration apparatus, characterized in that said apparatus comprises:
the image acquisition module is used for acquiring a foggy image and solving a dark channel image of the foggy image;
the light value calculation module is used for calculating the atmospheric light value of the foggy image according to the dark channel image;
the estimation calculation module is used for adopting non-local prior and the atmospheric light value to estimate the transmissivity of the image;
the calculation refinement module is used for performing refinement processing on the transmissivity by adopting guide filtering to obtain the processed transmissivity;
the image restoration module is used for restoring the foggy image into a fogless image according to the processed transmissivity and the atmospheric light value;
and the optimization output module is used for optimizing the haze-free image through a single-scale Retinex algorithm so as to output a defogged image.
9. A fog noise video image restoration apparatus, characterized in that the apparatus comprises: a memory, a processor and a fog noise video image restoration program stored on said memory and executable on said processor, said fog noise video image restoration program being configured to implement the steps of the fog noise video image restoration method as claimed in any one of claims 1 to 7.
10. A medium having stored thereon a fog noise video image restoration program which when executed by a processor implements the steps of the fog noise video image restoration method of any one of claims 1 to 7.
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| CN115861104B (en) * | 2022-11-30 | 2023-10-17 | 西安电子科技大学 | Remote sensing image defogging method based on transmissivity refinement |
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