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Proceedings Paper

Non-uniform correction of infrared image based on adaptive forgetting factor recursive least square method
Author(s): Chenlong Guo; Xining Liang; Yixuan Liu
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Paper Abstract

The traditional neural network method is faced with the problem of gradient step size selection when dealing with nonuniform correction of infrared images. When the gradient step size is large, it is easy to cause gradient divergence, and when the gradient step size is small, it is difficult to obtain convergence.At the same time, the algorithm is also prone to ghosting or image blurring. Aiming at this problem, this paper proposes an infrared image non-uniform correction method based on adaptive forgetting factor recursive least squares method. Firstly, this paper deduces the least squares method into the form of incremental calculation, and introduces it into the calculation of the offset and gain of nonuniform correction, so that it can train the infrared image frame by frame. At the same time, this paper considers the problem that the background of the previous frame is learned to generate ghosts in the process of image from long-term still to sudden change, and the calculation of forgetting factor is introduced. And this paper uses local structural similarity index (SSIM) to calculate the forgetting factor. The experimental results show that the iterative step size of the proposed method can be calculated adaptively, without manual adjustment, and can effect overcome the ghost problem. Compared with the traditional neural network method and time domain high-pass filtering method, the algorithm of this paper is the best.

Paper Details

Date Published: 27 November 2019
PDF: 6 pages
Proc. SPIE 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence, 1132117 (27 November 2019); doi: 10.1117/12.2548113
Show Author Affiliations
Chenlong Guo, Luoyang Electro-optical Equipment Research Institute (China)
Xining Liang, Luoyang Electro-optical Equipment Research Institute (China)
Yixuan Liu, Dalian Univ. of Technology (China)


Published in SPIE Proceedings Vol. 11321:
2019 International Conference on Image and Video Processing, and Artificial Intelligence
Ruidan Su, Editor(s)

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