[논문]Dual-Branch Structured De-Striping Convolution Network Using Parametric Noise Model

Lee, Jongho; Ro, Yong Man

doi:10.1109/access.2020.3019057

[해외논문] Dual-Branch Structured De-Striping Convolution Network Using Parametric Noise Model 원문보기

IEEE access : practical research, open solutions, v.8, 2020년, pp.155519 - 155528

Lee, Jongho (Image and Video Systems Laboratory, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea) , Ro, Yong Man (Image and Video Systems Laboratory, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea)

Abstract ▼ AI-Helper

The stripe fixed pattern noise (FPN) of infrared images significantly corrupts image quality, so that most infrared imaging systems suffer from the degradation of visibility 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 article, we propose the dual-branch structure based FPN de-striping deep convolutional neural network (DBS-DCN) to effectively extract structural features of FPN and preserve the image details in a single infrared image. In addition, we have established the parametric FPN model through the diagnostic experiments of infrared images based on the physical principle of an infrared detector and its 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 data, and our proposed method achieved outstanding de-striping results in both qualitative and quantitative evaluation compared to existing methods.

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DOI : 10.1109/ACCESS.2020.3019057 [무료]
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용어 설명: PCR은 세균 특이성이 있는 primer를 이용하여 적은 수의 세균이 있을지라도 쉽게 검출할 수 있는 유용한 방법이며, 이를 이용하여 구강 내 치면세균막이나 타액에서 직접 세균을 검출할 수 있게 되었다[8].

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