TY - GEN
T1 - Sure Based Convolutional Neural Networks for Hyperspectral Image Denoising
AU - Nguyen, Han V.
AU - Ulfarsson, Magnus O.
AU - Sveinsson, Johannes R.
N1 - Publisher Copyright: © 2020 IEEE.
PY - 2020/9/26
Y1 - 2020/9/26
N2 - This paper addresses the hyperspectral image (HSI) denoising problem by using Stein's unbiased risk estimate (SURE) based convolutional neural network (CNN). Conventional deep learning denoising approaches often use supervised methods that minimize a mean-squared error (MSE) by training on noisy-clean image pairs. In contrast, our proposed CNN-based denoiser is unsupervised and only makes use of noisy images. The method uses SURE, which is an unbiased estimator of the MSE, that does not require any information about the clean image. Therefore minimization of the SURE loss function can accurately estimate the clean image only from noisy observation. Experimental results on both simulated and real hyperspectral datasets show that our proposed method outperforms competitive HSI denoising methods.
AB - This paper addresses the hyperspectral image (HSI) denoising problem by using Stein's unbiased risk estimate (SURE) based convolutional neural network (CNN). Conventional deep learning denoising approaches often use supervised methods that minimize a mean-squared error (MSE) by training on noisy-clean image pairs. In contrast, our proposed CNN-based denoiser is unsupervised and only makes use of noisy images. The method uses SURE, which is an unbiased estimator of the MSE, that does not require any information about the clean image. Therefore minimization of the SURE loss function can accurately estimate the clean image only from noisy observation. Experimental results on both simulated and real hyperspectral datasets show that our proposed method outperforms competitive HSI denoising methods.
KW - Hyperspectral image denoising
KW - Stein's unbiased risk estimate
KW - convolutional neural network
KW - unsupervised deep learning
UR - https://www.scopus.com/pages/publications/85102016381
U2 - 10.1109/IGARSS39084.2020.9324734
DO - 10.1109/IGARSS39084.2020.9324734
M3 - Conference contribution
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 1784
EP - 1787
BT - 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020
Y2 - 26 September 2020 through 2 October 2020
ER -