Infrared video colorization method based on two-channel-circulation generative adversarial network

A dual-channel, colorized technology, applied in the field of image processing, can solve problems such as manual intervention, and achieve the effect of improving recognition and enhancing observability

Inactive Publication Date: 2018-07-24
DONGHUA UNIV
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Problems solved by technology

This method improves the accuracy of colorization, but the colorization result of this met

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  • Infrared video colorization method based on two-channel-circulation generative adversarial network
  • Infrared video colorization method based on two-channel-circulation generative adversarial network
  • Infrared video colorization method based on two-channel-circulation generative adversarial network

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Embodiment Construction

[0023] Below in conjunction with specific embodiment, further illustrate the present invention. It should be understood that these examples are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the teachings of the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the appended claims of the present application.

[0024] Embodiments of the present invention relate to an infrared video colorization method based on a dual-channel recurrent generation confrontation network, such as figure 1 As shown, it includes the following steps: collecting night vision infrared video data and color video data, the scene in the night vision infrared video data is the same as the scene in the color video data; put the video data set into the database afte...

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Abstract

The invention relates to an infrared video colorization method based on two-channel-circulation generative adversarial network. The method comprises the following steps of collecting night-vision infrared video data and color video data; carrying out frame extraction processing on a video data set and then putting into a database, and building a training set and a test set; constructing a two-channel-circulation generative adversarial deep learning network, and through a circulation training method, improving the abilities of generating a network G, generating a network F, discriminating a network DX, and discriminating a network DY; and acquiring an infrared image in an infrared video in real time, inputting the colorization results of the infrared image and the previous frame of infrared image into the trained and generated network G, and using the generated network G to carry out colorization processing on an infrared image sequence. In the invention, the colorization effect of a night-vision infrared video is enhanced, the observability of a colorization video is increased and manual intervention is not needed.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to an infrared video colorization method based on a dual-channel cyclic generative confrontation network. Background technique [0002] Infrared video colorization refers to endowing infrared image color and texture information in infrared video, which is essentially an image enhancement problem. Adding color and texture to infrared video can help people observe video content more intuitively, and can also help unmanned vehicles or robots better understand the environment. [0003] The traditional methods of infrared video colorization mainly include pseudo-colorization method, colorization method based on human intervention, colorization method based on template and colorization method based on multi-sensor fusion. However, the realization of these four types of traditional algorithms requires manual intervention, and the colorization results are quite different from the ...

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Application Information

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IPC IPC(8): G06T5/50G06N3/04G06N3/08
CPCG06N3/08G06T5/50G06T2207/10024G06T2207/10048G06T2207/10016G06T2207/20081G06T2207/20084G06N3/045
Inventor 李佳豪孙韶媛吴雪平
Owner DONGHUA UNIV
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