An Adaptive Deghosting Method in Neural Network-Based Infrared Detectors Nonuniformity Correction.

ABSTRACT: The problems of the neural network-based nonuniformity correction algorithm for infrared focal plane arrays mainly concern slow convergence speed and ghosting artifacts. In general, the more stringent the inhibition of ghosting, the slower the convergence speed. The factors that affect these two problems are the estimated desired image and the learning rate. In this paper, we propose a learning rate rule that combines adaptive threshold edge detection and a temporal gate. Through the noise estimation algorithm, the adaptive spatial threshold is related to the residual nonuniformity noise in the corrected image. The proposed learning rate is used to effectively and stably suppress ghosting artifacts without slowing down the convergence speed. The performance of the proposed technique was th

SUBMITTER: Li Y 

PROVIDER: S-EPMC5796467 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature