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A Noise Modeling Based Video Rain Removal Method

A video and noise technology, applied in the field of video rain removal based on noise modeling, can solve problems such as difficulty in obtaining

Active Publication Date: 2020-05-15
XI AN JIAOTONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

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 problem of rain removal; on the other hand, in order to obtain the special structural information of rain, many video rain removal methods (especially the more modern methods) often need to externally construct an effective Annotate the discriminative database to learn the structure of rain, and this information is often difficult to obtain for practical rain videos with specific structures

Method used

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  • A Noise Modeling Based Video Rain Removal Method
  • A Noise Modeling Based Video Rain Removal Method
  • A Noise Modeling Based Video Rain Removal Method

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Experimental program
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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 noise-modeling-based video deraining method that simultaneously estimates the rain streak noise component and the moving foreground in a video under the assumption of a low-rank background. First, acquire video data containing rain noise and initialize the model; establish a rain map generation model according to the characteristics of rain noise and video foreground; The formed rain strips have consistent directionality, and establish a prior distribution of small pieces of rain strips; establish a moving object detection model according to the characteristics of video foreground sparsity; convert the model into a rain removal model under the framework of maximum likelihood estimation; apply With rain video and rain removal model, the rain removal video and other statistical variables are obtained, and the rain removal video is output. The present invention aims to establish a high-quality video deraining model based on the principle of rain map generation and the structural characteristics of rain strip noise, and further more accurately enable the video deraining technology to be widely used in complex raining scenes with moving foregrounds.

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...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T5/00G06K9/62
CPCG06T5/002G06T2207/10016G06F18/2411G06F18/2415
Inventor 孟德宇谢琦赵谦魏玮易丽璇徐宗本
Owner XI AN JIAOTONG UNIV
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