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Night video enhancement method based on gradient integration

A video enhancement and gradient technology, applied in image enhancement, image data processing, instruments, etc., can solve problems such as inability to enhance video information, video image color drift, etc.

Active Publication Date: 2017-01-04
SHANGHAI UNIV
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Problems solved by technology

[0003] At present, the commonly used nighttime video enhancement methods in China include contrast-based video enhancement methods, filter-based video enhancement methods, wavelet transform-based video enhancement methods, and nighttime video enhancement methods based on frame brightness fusion. The first three types of methods are only in the existing Adjust the contrast, dynamic range, and gray value of the image based on the video image information. Such methods cannot enhance the lost video information.
The enhancement method based on frame brightness fusion belongs to the video frame fusion technology, but this algorithm is easy to cause the color drift of the video image when the static background brightness of the day is integrated into the night video frame.

Method used

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  • Night video enhancement method based on gradient integration
  • Night video enhancement method based on gradient integration
  • Night video enhancement method based on gradient integration

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Embodiment Construction

[0051] The specific embodiments of the present invention will be further described below in conjunction with the accompanying drawings.

[0052] Such as figure 1 As shown, a nighttime video enhancement method based on gradient fusion consists of the following steps:

[0053] Step 1: Download the night video video taken by the same surveillance camera night (i,j,l), where 1≤i≤M, 1≤j≤N, 1≤l≤L; and daytime video day (i,j,k), where 1≤i≤M, 1≤j≤N, 1≤k≤K; where i and j are the row and column coordinates of pixels in the nighttime video and daytime video images respectively, M, N represents the number of rows and columns of nighttime video and daytime video image respectively; l represents the frame number of nighttime video, L represents the frame number of nighttime video image, k represents the frame number of daytime video, and K represents the frame number of daytime video image; In the embodiment, M, N, L and K are 240, 320, 450 and 300, respectively.

[0054] Step 2: Using ...

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Abstract

The invention relates to a night video enhancement method based on gradient integration. Day background frames can be extracted by utilization of spatial constraint conditions and a Gaussian mixture model, and gradient information of day video background frames is integrated into night video gradient images, so the detailed information lost in the night video images can be compensated. Restored images can be obtained by use of a poisson equation and the boundary conditions of the day background frames. The video image brightness is enhanced, and moreover, a color fidelity effect is achieved.

Description

technical field [0001] The invention relates to a nighttime video enhancement method based on gradient fusion, and belongs to the technical field of video surveillance enhancement. Background technique [0002] Due to the low illumination conditions at night, the nighttime video images captured by the monitoring system will have relatively low resolution and some area information will be lost, making it difficult to extract effective information from nighttime videos. Enhancement of nighttime video is very important for video target detection and tracking and subsequent analysis. [0003] At present, the commonly used nighttime video enhancement methods in China include contrast-based video enhancement methods, filter-based video enhancement methods, wavelet transform-based video enhancement methods, and nighttime video enhancement methods based on frame brightness fusion. The first three types of methods are only in the existing The contrast, dynamic range, and gray value ...

Claims

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Application Information

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IPC IPC(8): G06T5/50G06T7/00
CPCG06T5/50G06T2207/10016G06T2207/20221G06T2207/30232
Inventor 孟新知黄微陈休聂龙保张婷婷
Owner SHANGHAI UNIV
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