Poisson-Gaussian joint noise image sequence separation and noise reduction method based on frame averaging and weight adjustment second-order blind recognition

Abstract

The invention discloses a Poisson-Gaussian joint noise image sequence separation and noise reduction method based on frame averaging and weight adjustment second-order blind recognition. The method comprises the following steps: S1, obtaining a noise image sequence; S2, dividing the noise image sequence into n groups, and preprocessing to obtain a new image sequence as an observation signal of analgorithm; S3, performing centralization processing on the image sequence to obtain a de-equalized hybrid observation matrix; S4, giving a non-zero time delay covariance matrix of the hybrid observation matrix in combination with the hybrid observation matrix; and S5, searching a unitary matrix approximate joint diagonalization covariance matrix by adopting a nonlinear least square method to obtain a weight matrix, and finally outputting an image after noise reduction. According to the method, effective information in the image is reserved as much as possible while the image noise is suppressed and eliminated, the overall operation time of the algorithm is shortened, and the instantaneity and effectiveness of the algorithm are fully guaranteed.

Description

technical field [0001] The invention relates to an image noise reduction method, in particular to a Poisson-Gaussian joint noise image sequence separation and noise reduction method based on frame average and weight adjustment second-order blind recognition, belonging to the field of Poisson-Gaussian joint noise image noise reduction . Background technique [0002] In people's daily work and life, information is mainly transmitted in the form of sound, image and video. Compared with the other two forms, image transmission is more intuitive and stable, and images can also be carried and Store more information, so today, images are still an important way of information transmission. [0003] In recent years, with the continuous development of social science and technology, people have a higher demand for information acquisition rate, and the requirements for image quality are also increasing. The higher the image quality, the more effective information is obtained. At presen...

AI Technical Summary

Problems solved by technology

However, in the actual application process of the traditional image noise reduction method, there are problems such as difficult processing, long processing time, and easy loss of detailed information, and the use effect has not been able to meet expectations.

Images