Ghosting reduction in adaptive non-uniformity correction of infrared focal-plane array image sequences

R. Esteban Vera, I. Sergio Torres

Resultado de la investigación: Contribución a una conferenciaArtículorevisión exhaustiva

12 Citas (Scopus)

Resumen

Non-uniformity correction is a critical task for achieving higher performances in modern infrared imaging systems. Lately, special interest has been given to a scene-based adaptive non-uniformity correction approach based on a neural network with a steepest descent learning rule. However, low motion and some scene artifacts such as edges usually cause the production of ghosting-like artifacts over the output images. We assume that such ghosting is mainly produced by the use of fixed learning rates. In this work we propose the addition of two adaptive learning rate strategies to minimize the presence of ghosting artifacts. The first proposal relies in the fact that a momentum term can accelerate and stabilize a learning process, which in this case could lead to a possible ghosting reduction as well. As an alternative to the momentum, we also propose the use of a variable learning rate related to the local variance of the input image. The both proposed improvements are tested in infrared image sequences with simulated non-uniformity. Results demonstrate that the proposed approaches are helpful in increasing the quality of the corrected image sequences. Nonetheless, only the variable learning rate seems to reduce in a better way the unwanted ghosting artifacts.

Idioma originalInglés
Páginas1001-1004
Número de páginas4
EstadoPublicada - 2003
Publicado de forma externa
EventoProceedings: 2003 International Conference on Image Processing, ICIP-2003 - Barcelona, Espana
Duración: 14 sep 200317 sep 2003

Conferencia

ConferenciaProceedings: 2003 International Conference on Image Processing, ICIP-2003
País/TerritorioEspana
CiudadBarcelona
Período14/09/0317/09/03

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