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Subject [IEEE Access] Dual-Branch Structured De-Striping Convolution Network Using Parametric Noise Model (by Jongho Lee) is accepted in IEEE Access
Date 2020-08-24
Title: Dual-Branch Structured De-Striping Convolution Network Using Parametric Noise Model
Authors: Jongho Lee and Yong Man Ro
Abstract: The stripe fixed pattern noise (FPN) of the infrared image significantly corrupts the image quality so that the infrared imaging system suffers from the degradation of observability and detectability during operation. Therefore, the FPN de-striping method, which eliminates stripe patterns without substantial loss of image information, remains a core technology in the field of infrared image processing. In this paper, we propose the dual-branch structured based FPN de-striping deep convolutional neural network to effectively extract the structural features of the FPN and preserve the image details in the single infrared image. In addition, we have established the parametric FPN model through an infrared image diagnosis experiment based on the physical principle of the infrared detector signal response. We have optimized each parameter of the FPN model using measured data, which acquired on a wide range of detector temperatures. Further, we generate the training data using our FPN model to ensure stable learning performance against various stripe patterns. We performed comparative experiments with state-of-the-art methods using artificially corrupted infrared images and real corrupted infrared images, and our proposed method achieved outstanding de-striping results in both qualitative and quantitative evaluation compared with existing methods.