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Synchronous rain and fog synthesis and removal method and device in image
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A synthesis method and technology in images, applied in the field of image processing, can solve the problems of poor image processing effect in real rain
Active Publication Date: 2017-11-03
BEIJING UNIV OF POSTS & TELECOMM
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Therefore, this method does not work well for real rainy images
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[0073] figure 1 The first method for synchronous synthesis of rain and fog in an image provided by the embodiment of the present application, the method includes:
[0074] S101, selecting any real image without rain and fog as a reference image;
[0075] Wherein, the real image without rain and fog refers to a real photo taken on a sunny day.
[0076] S102, the reference image is processed through the rainy image model RRM, and the rainy and foggy image J close to the real scene is synthesized, wherein the RRM model is: J=I+R*H, and I and R are respectively the reference image without rainy fog and Rain line, H is the image model of fog, * represents convolution.
[0077] Wherein, the above-mentioned processing of the reference image through the rainy image model RRM means that the reference image is used as the input of the RRM model, processed by the RRM model, and the synthesized rainy and foggy image J close to the real scene is output.
[0078] It is worth mentioning t...
Embodiment approach
[0080] One of the above implementations includes:
[0081] The reference image I is converted from the RGB color space to the brightness and chromaticityYCbCr space to obtain an image in the YCbCr space;
[0082] According to the image of the YCbCr space, the rain line R is superimposed on the Y channel of the YCbCr space to synthesize an image with the rain line;
[0083] Convert the YCbCr image with rain lines to RGB space to obtain an RGB image with rain lines;
[0084] Calculate the transmittance t(x) of the reference image to obtain the image model H(x) of the fog, where x is a pixel in the reference image;
[0085] The RGB image with rain lines and the fog image model H(x) are processed to synthesize a composite image J with rain and fog.
[0086] Among them, according to the image in the YCbCr space, the rain line R is superimposed on the Y channel of the YCbCr space, and an implementation method for synthesizing an image with rain lines can use the rain line Garg et...
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Abstract
The embodiment of the invention provides a synchronous rain and fog synthesis and removal method and a device in an image. The synthesis method comprises steps: any real image with no rain and no fog is selected as a reference image; and the reference image is fused through a rain image model RRM, and an image with a rain and a fog close to a real scene is synthesized. The removal method comprises steps: multiple images with the rain and the fog synthesized are selected as a training set; a totally convolutional neural network is trained, and a totally convolutional neural network after training is acquired; and a photographed real image with rain is acquired, the photographed real image with rain is inputted to the totally convolutional neural network after training, and an image after rain removal is outputted. The method realizes problems of synchronous rain and fog synthesis and removal in the image.
Description
technical field [0001] The invention belongs to the technical field of image processing, in particular to a method and device for synchronizing and removing rain and fog in an image. Background technique [0002] Taking the intelligent monitoring system for outdoor scenes as an example, the existing intelligent monitoring system first collects images through the camera, and then analyzes the image content through various computer vision algorithms, including the identification and tracking of specific targets in the image, and abnormal events such as fights detection. Such vision algorithms usually require accurate extraction of image features, so they have high requirements for image quality. [0003] However, the weather factor is beyond the control of the monitoring system. Rain will inevitably cause the degradation of image quality, which will affect the accuracy of image feature extraction, and lead to the performance degradation or even complete failure of the visual ...
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