Self-adaptive traffic video real-time defogging method based on temporal-spatial coherence

A spatio-temporal correlation and self-adaptive technology, applied in image data processing, instrumentation, computing and other directions, can solve the problems of ratio distortion, inability to adapt to the degree of haze interference, poor real-time performance, etc., to achieve the effect of accelerating fog removal

Inactive Publication Date: 2015-10-14
ZHEJIANG UNIV OF TECH
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Problems solved by technology

[0004] On the whole, the existing image defogging or video defogging algorithms mainly have three problems: poor real-time performance, distorted contrast after restoration, and inability to eliminate the influence of dense fog
The essence of the latter two problems is that the image defogging algorithm cannot adapt to images with different levels of haze interference.

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  • Self-adaptive traffic video real-time defogging method based on temporal-spatial coherence
  • Self-adaptive traffic video real-time defogging method based on temporal-spatial coherence
  • Self-adaptive traffic video real-time defogging method based on temporal-spatial coherence

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Embodiment Construction

[0080] The present invention will be further described below with reference to the accompanying drawings.

[0081] The main steps of the self-adaptive traffic video real-time defogging method based on spatio-temporal correlation of the present invention are as follows: figure 1 shown.

[0082] Step 1. Using the time continuity of the traffic video, set up a time slice, estimate the lane space area of ​​the initial frame of the time slice, the haze influence sign value T, the initial transmittance correction value X and the transmittance distribution; other images in this period of time In the process of defogging and restoring the frame, the parameters obtained from the calculation of the initial frame are used.

[0083] Generally, traffic videos are captured by surveillance cameras fixed on the road, and the scene is relatively uniform for a period of time. Therefore, when considering temporal continuity, traffic videos within a time segment can be analyzed as a whole. Ther...

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Abstract

The invention relates to a self-adaptive traffic video real-time defogging method based on temporal-spatial coherence. The method comprises the steps: S1, estimating a lane space region of a time slice initial frame, a haze influence mark value T, an initial transmissivity correction value X and a transmissivity distribution condition; S2, extracting a straight line lane in a video and determining a limited lane region; S3, searching for a region with the minimum haze interference from an original image to obtain a brightest pixel value as atmospheric light intensity; S4, dividing space ranges, wherein a mark camera is arranged in each space range and calculating the haze influence mark value T, the image contrast and the initial transmissivity correction value by using an image shot by the camera and applying those to other cameras in the region; S5, obtaining the transmissivity distribution of the optimal transmissivity of each image block and optimizing the blocky transmissivity distribution by using a guide filter; and S6, solving the original image pixel value in the lane space region and reducing a haze-free image of the lane part.

Description

technical field [0001] The invention relates to a real-time defogging method for traffic video. Background technique [0002] Methods based on video processing and vision technology are more and more widely used in traffic monitoring systems, but because traffic videos are shot in outdoor scenes, they are often affected by changeable weather. At present, foggy weather occurs frequently, and the traffic video images acquired in the foggy environment have blurred and degraded phenomena, resulting in low image clarity, which significantly affects various services of traffic video image processing, including vehicle detection, vehicle target, etc. In operations such as feature extraction and vehicle tracking, there are problems such as the inability to effectively identify vehicles and the difficulty in extracting detailed features of vehicles. After analyzing the characteristics of haze traffic video images, it is found that only by improving image clarity and removing haze in...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00G06T5/50
CPCG06T5/00G06T5/003G06T5/50G06T2207/10004G06T2207/10016G06T2207/20012G06T2207/20021G06T2207/20182G06T2207/30232G06T2207/30236
Inventor 董天阳吴佳敏范菁曹斌
Owner ZHEJIANG UNIV OF TECH
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