Acta Optica Sinica, Volume. 39, Issue 6, 0610003(2019)

Neural Network-Based Noise Suppression Algorithm for Star Images Captured During Daylight Hours

Yuchen Liu*, Chunhui Zhao, and Qing Xu
Author Affiliations
  • Beijing Institute of Control Engineering, China Academy of Space Technology, Beijing 100190, China
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    References(34)

    [3] Xu Q, Zhao C H, Li X. Stellar radiation modeling and image simulation for airborne daytime star sensor. [C]∥2016 IEEE International Conference on Signal and Image Processing (ICSIP), August 13-15, 2016, Beijing, China. New York: IEEE, 630-635(2016).

         Xu Q, Zhao C H, Li X. Stellar radiation modeling and image simulation for airborne daytime star sensor. [C]∥2016 IEEE International Conference on Signal and Image Processing (ICSIP), August 13-15, 2016, Beijing, China. New York: IEEE, 630-635(2016).

    [10] Zhang K, Zuo W M, Chen Y J et al. Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising[J]. IEEE Transactions on Image Processing, 26, 3142-3155(2017).

         Zhang K, Zuo W M, Chen Y J et al. Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising[J]. IEEE Transactions on Image Processing, 26, 3142-3155(2017).

    [11] Chen Y J, Pock T. Trainable nonlinear reaction diffusion: a flexible framework for fast and effective image restoration[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 1256-1272(2017).

         Chen Y J, Pock T. Trainable nonlinear reaction diffusion: a flexible framework for fast and effective image restoration[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 1256-1272(2017).

    [12] Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift. [C]∥Proceedings of the 32nd International Conference on International Conference on Machine Learning, July 6-11, 2015, Lille France. Cambridge: JMLR. org, 37, 448-456(2015).

         Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift. [C]∥Proceedings of the 32nd International Conference on International Conference on Machine Learning, July 6-11, 2015, Lille France. Cambridge: JMLR. org, 37, 448-456(2015).

    [13] He K M, Zhang X Y, Ren S Q et al. Identity mappings in deep residual networks[M]. ∥Leibe B, Matas J, Sebe N, et al. Computer Vision-ECCV 2016. Cham: Springer, 9908, 630-645(2016).

         He K M, Zhang X Y, Ren S Q et al. Identity mappings in deep residual networks[M]. ∥Leibe B, Matas J, Sebe N, et al. Computer Vision-ECCV 2016. Cham: Springer, 9908, 630-645(2016).

    [14] Shi W Z, Caballero J, Huszár F et al. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition, June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 1874-1883(2016).

         Shi W Z, Caballero J, Huszár F et al. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition, June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 1874-1883(2016).

    [15] Zhang Y M, Chen T. Efficient inference for fully-connected CRFs with stationarity. [C]∥2012 IEEE Conference on Computer Vision and Pattern Recognition, June 16-21, 2012, Providence, RI, USA. New York: IEEE, 582-589(2012).

         Zhang Y M, Chen T. Efficient inference for fully-connected CRFs with stationarity. [C]∥2012 IEEE Conference on Computer Vision and Pattern Recognition, June 16-21, 2012, Providence, RI, USA. New York: IEEE, 582-589(2012).

    [16] Dabov K, Foi A, Katkovnik V et al. Image denoising by sparse 3-D transform-domain collaborative filtering[J]. IEEE Transactions on Image Processing, 16, 2080-2095(2007).

         Dabov K, Foi A, Katkovnik V et al. Image denoising by sparse 3-D transform-domain collaborative filtering[J]. IEEE Transactions on Image Processing, 16, 2080-2095(2007).

    [17] Aharon M, Elad M, Bruckstein A. K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation[J]. IEEE Transactions on Signal Processing, 54, 4311-4322(2006).

         Aharon M, Elad M, Bruckstein A. K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation[J]. IEEE Transactions on Signal Processing, 54, 4311-4322(2006).

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    Yuchen Liu, Chunhui Zhao, Qing Xu. Neural Network-Based Noise Suppression Algorithm for Star Images Captured During Daylight Hours[J]. Acta Optica Sinica, 2019, 39(6): 0610003

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    Paper Information

    Category: Image Processing

    Received: Jan. 18, 2019

    Accepted: Mar. 12, 2019

    Published Online: Jun. 17, 2019

    The Author Email: Liu Yuchen (lyc133@163.com)

    DOI:10.3788/AOS201939.0610003

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