Abstract

In scene-based nonuniformity correction algorithms, artificial ghosting and image blurring degrade the correction quality severely. In this paper, an improved algorithm based on the diamond search block matching algorithm and the adaptive learning rate is proposed. First, accurate transform pairs between two adjacent frames are estimated by the diamond search block matching algorithm. Then, based on the error between the corresponding transform pairs, the gradient descent algorithm is applied to update correction parameters. During the process of gradient descent, the local standard deviation and a threshold are utilized to control the learning rate to avoid the accumulation of matching error. Finally, the nonuniformity correction would be realized by a linear model with updated correction parameters. The performance of the proposed algorithm is thoroughly studied with four real infrared image sequences. Experimental results indicate that the proposed algorithm can reduce the nonuniformity with less ghosting artifacts in moving areas and can also overcome the problem of image blurring in static areas.

© 2016 Optical Society of America

Scene-based nonuniformity correction algorithm based on interframe registration

Chao Zuo, Qian Chen, Guohua Gu, and Xiubao Sui
J. Opt. Soc. Am. A 28(6) 1164-1176 (2011)

Scene-based nonuniformity correction technique for infrared focal-plane arrays

Yong-Jin Liu, Hong Zhu, and Yi-Gong Zhao
Appl. Opt. 48(12) 2364-2372 (2009)

Kalman filtering for adaptive nonuniformity correction in infrared focal-plane arrays

Sergio N. Torres and Majeed M. Hayat
J. Opt. Soc. Am. A 20(3) 470-480 (2003)

Spatial and temporal adaptive nonuniformity correction for infrared focal plane arrays

Lingfei Song and Hua Huang
Opt. Express 30(25) 44681-44700 (2022)

An algebraic algorithm for nonuniformity correction in focal-plane arrays

Bradley M. Ratliff, Majeed M. Hayat, and Russell C. Hardie
J. Opt. Soc. Am. A 19(9) 1737-1747 (2002)