Video de-noising method based on structure tensor and Kalman filtering

Abstract

The invention provides a video real-time de-noising method based on a structure tensor and Kalman filtering. The method includes the following steps that an image frame to be processed at the current moment is acquired, and n frames of images which are before the current frame and have been stored and de-noised; the image frame to be processed at the current moment is pre-filtered with an average filter; based on the structure tensor, movement of the current image frame is estimated by fully utilizing close time and space relations between the image frame to be processed at the current moment and adjacent image frames before the image frame to be processed at the current moment; based on the movement estimation result, de-noising is performed in a time domain with a Kalman filtering method; de-noising is performed in a space domain with a Wiener filtering method; two de-noised images are synthesized, and the final de-noised image is acquired through weighting. With the method, high-noise video can be de-noised, and a good de-noising effect is achieved; besides, because complicated interactive calculation does not exist, achievement of hardware such as FPGAs is facilitated, and real-time the high-noise video can be de-noised.

Description

Technical field: [0001] The invention belongs to the field of video processing, and mainly relates to video denoising, in particular to a real-time video denoising method based on structural tensor and Kalman filter, which can be used for real-time denoising of naturally noisy videos. Background technique: [0002] With the rapid development of digital photoelectric imaging technology, digital photoelectric imaging equipment has been widely used in computational photography, security monitoring, robot navigation and military reconnaissance and other fields. Usually, the sensors of digital photoelectric imaging equipment are composed of CCD or CMOS. During the imaging process, due to the influence of optical noise, noise of components such as resistors and capacitors, sensor noise, circuit noise, etc., the output image will inevitably contain Many noises, these noises not only destroy the real information of the image, but also seriously affect the visual effect of the image....

AI Technical Summary

Problems solved by technology

[0008] Aiming at the problem that the above-mentioned prior art cannot effectively denoise the video with high noise in real time, the present invention proposes a real-time video denoising method based on structure tensor and Kalman filter, which can denoise the video in real time. Noise reduction processing, and has a good denoising effect

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