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

Yuchen Liu; Chunhui Zhao; Qing Xu

doi:10.3788/AOS201939.0610003

[1] Fan Q Y, Li X J. Selection of optical-system parameters for an all-day used star sensor[J]. Acta Optica Sinica, 31, 1122001(2011).

Fan Q Y, Li X J. Selection of optical-system parameters for an all-day used star sensor[J]. Acta Optica Sinica, 31, 1122001(2011).

[2] Liu Y, Dai D K, Ma L H et al. A star sensor calibration method based on attitude correlated frame[J]. Acta Optica Sinica, 37, 1128003(2017).

Liu Y, Dai D K, Ma L H et al. A star sensor calibration method based on attitude correlated frame[J]. Acta Optica Sinica, 37, 1128003(2017).

[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).

[4] Duncan T M. A daylight stellar sensor using a charge-coupled device[J]. Proceedings of SPIE, 1111, 2-14(1989). http://spie.org/Publications/Proceedings/Paper/10.1117/12.977965

Duncan T M. A daylight stellar sensor using a charge-coupled device[J]. Proceedings of SPIE, 1111, 2-14(1989). http://spie.org/Publications/Proceedings/Paper/10.1117/12.977965

[5] Jiang P, Sun T F. Infrared star target detection based on maximum energy in daytime[J]. Infrared, 34, 34-38(2013).

Jiang P, Sun T F. Infrared star target detection based on maximum energy in daytime[J]. Infrared, 34, 34-38(2013).

[6] Hu X D, Hu Q, Lei X et al. Method of star centroid extraction used in daytime star sensors[J]. Journal of Chinese Inertial Technology, 22, 481-485(2014).

Hu X D, Hu Q, Lei X et al. Method of star centroid extraction used in daytime star sensors[J]. Journal of Chinese Inertial Technology, 22, 481-485(2014).

[7] Wei W, Liu E H. Preprocessing of infrared star map and position accuracy analysis of star point[J]. Infrared and Laser Engineering, 43, 991-996(2014).

Wei W, Liu E H. Preprocessing of infrared star map and position accuracy analysis of star point[J]. Infrared and Laser Engineering, 43, 991-996(2014).

[8] Wang W J, Wei X G, Li J et al. Noise suppression algorithm of short-wave infrared star image for daytime star sensor[J]. Infrared Physics & Technology, 85, 382-394(2017). http://www.sciencedirect.com/science/article/pii/S1350449517301317

Wang W J, Wei X G, Li J et al. Noise suppression algorithm of short-wave infrared star image for daytime star sensor[J]. Infrared Physics & Technology, 85, 382-394(2017). http://www.sciencedirect.com/science/article/pii/S1350449517301317

[9] Han L, Cai H. Analysis of stargazing capability of star sensors from the atmosphere in daytime[J]. Journal of Spacecraft TT & C Technology, 34, 291-297(2015).

Han L, Cai H. Analysis of stargazing capability of star sensors from the atmosphere in daytime[J]. Journal of Spacecraft TT & C Technology, 34, 291-297(2015).

[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).