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Proceedings Paper

Neural network non-uniformity correction for eliminating low frequency noise
Author(s): Qian Li; Bo Yang; Xiaodong Wang; Lidong Liu; Chuanming Liu; Junbo Su
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Paper Abstract

Classic neural network algorithm can not correct the low frequency noise,and it is easy to appearance ghost artifact phenomenon. In this paper,we present a new improved algorithm which can eliminate the low frequency noise and ghost artifact.We add a learning layer which can preprocess to the input layer.In this layer, use the Gaussian filter folding the down sampling image and resampling to the image so that the new input image have global correlation, it could assure the accuracy of the estimate real scene image,this can help us to eliminate the low frequency noise in the input image. Then we judged the frame motion by the edge detection, setting a threshold to compare with the MSE of the edge frame difference.Considering the result of the comparison,we use the difference step size to update the gain and offset parameters. Experimental results show that the new algorithm can improve image quality effectively

Paper Details

Date Published: 7 November 2018
PDF: 10 pages
Proc. SPIE 10832, Fifth Conference on Frontiers in Optical Imaging Technology and Applications, 1083207 (7 November 2018); doi: 10.1117/12.2505904
Show Author Affiliations
Qian Li, Kunming Institute of Physics (China)
Bo Yang, Kunming Institute of Physics (China)
Xiaodong Wang, Kunming Institute of Physics (China)
Lidong Liu, Kunming Institute of Physics (China)
Chuanming Liu, Kunming Institute of Physics (China)
Junbo Su, Kunming Institute of Physics (China)


Published in SPIE Proceedings Vol. 10832:
Fifth Conference on Frontiers in Optical Imaging Technology and Applications
Junhao Chu; Wenqing Liu; Huilin Jiang, Editor(s)

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