Method for processing infrared digital video signal at night

Abstract

The invention discloses a method for processing an infrared digital video signal at night, which comprises the following steps of: firstly, converting a monitoring video image represented by RGB color codes into an image represented by YUV color codes to acquire the component Y of a gray value in the monitoring image represented by the YUV color codes; secondly, in a loop filtering link, performing filtering on the component Y of the gray value of an image pixel represented by the YUV color codes through a mathematic morphological algorithm, performing an erosion operation on the image converted into the format of YUV color codes by the mathematic morphological algorithm and by using open operation according to the property of the mathematic morphological algorithm, and then performing a dilation operation on the image; and finally, performing software filtering on the component Y subjected to the software filtering of the mathematic morphological algorithm by a median filtering algorithm. In the method, the monitoring image at night is subjected to denoising enhancement by combining the mathematic morphological algorithm and the median filtering algorithm, and practice tests prove that the method has the excellent denoising effect.

Description

technical field [0001] The invention relates to a processing method for converting a nighttime camera signal into a video signal, which belongs to the improvement of a loop filter method in a nighttime digital video signal processing flow, in particular to a nighttime infrared digital video signal processing method. Background technique [0002] At present, DVR (digital video recorder) monitoring system has been widely used in various fields, especially in the application of safe city and safe community. When it is used at night due to insufficient illumination, in order to obtain better results, infrared lights are generally used Fill light, the illuminance of infrared fill light cannot reach the daytime illuminance. When the light intensity is low, the monitoring image captured by the camera will contain randomly distributed white point snowflake noise, and the noise will increase as the illuminance decreases. At the same time, due to When the light intensity decreases at ...

AI Technical Summary

Problems solved by technology

The popular DVRs on the market now generally use the H.264 standard to encode the surveillance video stream, and use the solution of an embedded curing system. As a result, the following three problems will appear: ① Severe distortion when the DVR plays back data; The noise is amplified into "edelweiss". If the bit rate is limited, it will even cause a full screen of flowers, and severe distortion makes it impossible to see people's faces clearly.

As a security system application, the clear requirements for data playback at night are higher than those during the day; ②DVR video hard disk takes up a lot of space: the data volume of the video stream containing noise is 30-50% larger than the normal code stream, or even several times

③The video playback of fast-moving objects (such as cars) by DVR is seriously distorted

[0025] Using mathematical morphology method for denoising enhancement can remove part of the noise, and because the opening operation and closing operation have the function of clustering, it can gather together the image information with similar gray scale and dominant quantity in a certain range in the monitoring image , so that the image information is not easy to lose during denoising, so a clearer image can be obtained, but its disadvantage is that the noise removal is not thorough enough, and the image after denoising is not smooth enough

[0026] Using the median filter method for denoising enhancement can obtain a good denoising effect, making the denoising and enhancing monitoring image smooth, so that the compressed and synthesized monitoring image becomes very smooth, so that the compressed and synthesized monitor image becomes very smooth. The data volume of the monitoring code stream is greatly reduced; however, since the noise in the monitoring image at night is not much different from the brightness value of the surrounding image information, the image after denoising is not clear enough, and more images will be lost

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