An Adaptive Noise Reduction Method for Low-light Video Images Based on Gradient-Guided Filtering

Abstract

The invention discloses a low-light video image self-adaptive noise reduction method based on gradient guide filtering, and belongs to the technical field of image processing. The method comprises thefollowing steps: step 1, detecting a motion region in a low-light video image; step 2, estimating the noise intensity of the low-light video image; step 3, carrying out self-adaptive gradient guide filtering and iterative guide filtering processing on the low-light video image, so that a final filtered output image is obtained. The invention aims to provide a low-light video image self-adaptive noise reduction method based on gradient guide filtering, on the premise that human intervention is reduced as much as possible, the image noise is reduced, the image signal-to-noise ratio is improved,and the image quality is improved. The method has a good practical application prospect in the field of low-light night vision imaging.

Description

technical field [0001] The invention relates to a gradient-guided filter-based self-adaptive noise reduction method for low-light video images, which belongs to the technical field of image processing. Background technique [0002] Low-light night vision imaging is one of the important methods commonly used in the field of night vision. The inherent random noise of low-light night vision images is a key factor affecting the imaging quality. Low-light night vision image filtering has also become a long-term research topic at home and abroad. [0003] At present, the main low-light imaging devices for night vision include: image tube coupled CCD (ICCD), electron multiplier CCD (EMCCD), electron bombardment CCD (EBCCD), high-sensitivity CMOS, etc. Since the intensity of visible light at night is relatively weak, not only high-sensitivity photosensitive devices are required, but also in order to obtain as much scene detail information as possible, the photoelectric conversion si...

AI Technical Summary

Problems solved by technology

The human eye is very sensitive to noise, and a large amount of noise will seriously reduce the imaging quality and affect the normal observation of people. Therefore, how to effectively reduce the noise in night vision low-light imaging has always been a key technology in the research of night vision imaging technology.

[0004] Typical simple methods of image noise reduction mainly include: mean filter, median filter, bilateral filter, etc. Although the method is simple and easy to implement, the effect is often poor; typical complex methods mainly include: non-local mean (NLMS) method, Bay Yaesian least squares-Gaussian scale mixture method (BLS-GSM), three-dimensional block matching (BM3D) method, etc., although the effect is good, but the algorithm is complex, the amount of calculation is large, and it is difficult to apply in real time

[0005] In summary, how to efficiently reduce noise in low-light video images, and that can run fast and in real-time, remains a challenging problem

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