Guided filter and adaptive learning rate based non-uniformity correction algorithm for infrared focal plane array

详细信息    查看全文

• 作者：Rong Sheng-Hui^a ; Zhou Hui-Xin^a ; ^{hxzhou@mail.xidian.edu.cn" class="auth_mail" title="E-mail the corresponding author} ; Qin Han-Lin^a ; Lai Rui^b ; Qian Kun^a
• 关键词：Infrared focal plane array ; Non-uniformity correction ; Neural network ; Guided filter ; Adaptive learning rate
• 刊名：Infrared Physics & Technology
• 出版年：2016
• 出版时间：May 2016
• 年：2016
• 卷：76
• 期：Complete
• 页码：691-697
• 全文大小：1617 K

文摘

Imaging non-uniformity of infrared focal plane array (IRFPA) behaves as fixed-pattern noise superimposed on the image, which affects the imaging quality of infrared system seriously. In scene-based non-uniformity correction methods, the drawbacks of ghosting artifacts and image blurring affect the sensitivity of the IRFPA imaging system seriously and decrease the image quality visibly. This paper proposes an improved neural network non-uniformity correction method with adaptive learning rate. On the one hand, using guided filter, the proposed algorithm decreases the effect of ghosting artifacts. On the other hand, due to the inappropriate learning rate is the main reason of image blurring, the proposed algorithm utilizes an adaptive learning rate with a temporal domain factor to eliminate the effect of image blurring. In short, the proposed algorithm combines the merits of the guided filter and the adaptive learning rate. Several real and simulated infrared image sequences are utilized to verify the performance of the proposed algorithm. The experiment results indicate that the proposed algorithm can not only reduce the non-uniformity with less ghosting artifacts but also overcome the problems of image blurring in static areas.