Method for removing rain in video based on noise modeling
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A video and noise technology, applied in the field of video rain removal based on noise modeling, can solve problems such as difficult to obtain
Active Publication Date: 2018-04-13
XI AN JIAOTONG UNIV
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On the one hand, this method focuses on the description of the structural information of the rain in the video, but does not fully consider other information in the video, such as the prior structural knowledge of the foreground object and the background scene, so it does not take advantage of the beneficial structural knowledge of the non-rain part of the video. Complementary effect on the
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Embodiment 1
[0110] The rain video data shown in Figure 2(a) is used as the experimental object of the present invention, and the video is a real rain video taken in a static scene without moving objects. The size of the video data is 288×368×171, the mixture component fraction of Gaussian mixed with small blocks is 3, the maximum number of iteration steps is 40, the size of Gaussian blocks is 2×2, and the background rank is 2.
[0111] see figure 1 , the process is as follows:
[0112] Step S1: read the original video, and initialize each statistical variable and parameter of the model;
[0113] Step S2: set up the statistical model that rain strip generates according to the characteristics of video foreground, background and rain noise;
[0114] D=H⊙D+H ⊥ ⊙D
[0115] f(H ⊥ ⊙D)=f(H ⊥ ⊙UV T )+E
[0116]
[0117] Among them, D is the input video data, H is the support of the video foreground, and E is the rain noise. After being converted into a small block, its i-th column repre...
Embodiment 2
[0168] The rain video data shown in Figure 6 (a)) is used as the experimental object of the present invention, which is a real rain video with moving foreground (people, cars) taken in a static scene. The size of the video data is 240×360×119, the mixture fraction of Gaussian mixed with small blocks is 3, the maximum number of iteration steps is 40, and the size of the Gaussian block is 2×2.
[0169] Step S1: read the original video, and initialize each statistical variable and parameter of the model;
[0170] Step S2: set up the statistical model that rain strip generates according to the characteristics of video foreground, background and rain noise;
[0171] D=H⊙D+H ⊥ ⊙D
[0172] f(H ⊥ ⊙D)=f(H ⊥ ⊙UV T )+E
[0173]
[0174] Among them, D is the input video data, H is the support of the video foreground, and E is the rain noise. After being converted into a small block, its i-th column represents the noise on the i-th small block in the video. The high-dimensional Ga...
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Abstract
A method for removing rain in a video based on noise modeling is disclosed. Under the assumption of a low-rank background, the rain bar noise component and the moving foreground in the video are simultaneously estimated. First, video data containing rain noise is acquired and a model is initialized; a rain map generation model is created according to the characteristics of the rain noise and the video foreground; the structural characteristics of the rain imaging in the video-a rain bar formed by moving rain droplets on each small block in an image is identical in the direction, the small block prior distribution of the rain bar is established; a moving object detection model is established according to the characteristics of the video foreground sparsity; the model is converted into a rain removal model under the maximum likelihood estimation framework; a rain-containing video and the rain removal model are applied to get a rain-removed video and other statistical variables, and the rain-removed video is output. The method aims to build a high-quality video rain removal model based on a rain map generation principle and rain bar noise structure characteristics, thereby more accurately allowing the video rain removal technology to be widely applied to complex raining scenes with the moving foreground.
Description
technical field [0001] The invention relates to a video image processing technology for shooting images outdoors, in particular to a video rain removal method based on noise modeling. Background technique [0002] Because the shooting quality of the outdoor shooting system is often affected by bad weather (such as rain, snow, fog), the details of the captured video or image are destroyed, the texture is blurred, and the background part is blocked by highlighted raindrops and rain stripes, so it cannot Use the captured images for further processing operations, such as feature extraction, target recognition, etc. Therefore, removing rain and snow from video images has become a technology that has emerged in the field of computer vision in recent years. On the premise of preserving the details of the video image, the deraining technology preprocesses the damaged video image to restore the quality of the affected video image to the maximum extent, allowing computer vision algor...
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