Nonlinear distortion image correction from confocal microscope based on interpolation

Bao Xuejing; Dai Shijie; Guo Cheng; Lv Shoudan; Shen Cheng; Liu Zhengjun

doi:10.3788/IRLA201746.1103006

Through the analysis of confocal microscope in the imaging process caused by the position, such as optical hardware deviation converge and pinhole position deviation occurring in image distortion phenomenon, a position correction function into interpolation algorithm was proposed for nonlinear distortion image correction and rehabilitation. The convolution neural network based on machine learning technology was applied to improve the quality of image position correction after degradation when training a single image. The five layers of convolution and down sampling to join pooling layer were employed to reduce the order of magnitude in network parameters. The results show that the pooling layer can improve the operation speed significantly and improve the sharpness of the image.

Nonlinear distortion image correction from confocal microscope based on interpolation

1. Department of Automatic Test and Control,Harbin Institute of Technology,Harbin 150001,China;

2. Micro and Nanotechnology Research Center,Harbin Institute of Technology,Harbin 150001,China

Received Date: 2017-10-10

Rev Recd Date: 2017-11-20

Publish Date: 2017-11-25

Key words:

Abstract: Through the analysis of confocal microscope in the imaging process caused by the position, such as optical hardware deviation converge and pinhole position deviation occurring in image distortion phenomenon, a position correction function into interpolation algorithm was proposed for nonlinear distortion image correction and rehabilitation. The convolution neural network based on machine learning technology was applied to improve the quality of image position correction after degradation when training a single image. The five layers of convolution and down sampling to join pooling layer were employed to reduce the order of magnitude in network parameters. The results show that the pooling layer can improve the operation speed significantly and improve the sharpness of the image.

HTML

Reference (15)

[1]	Ni K S, Nguyen T Q. Image super-resolution using support vector regression[J]. IEEE Transactions on Image Processing, 2007, 16(6):596-610.
[2]	Pan Y, Liu R, Guan B, et al. Accurate depth extraction method for multiple light-coding based depth cameras[J]. IEEE Transactions on Multimedia, 2017, 19(4):685-701.
[3]	Lu T, Xiong Z, Zhang Y, et al. Robust face super-resolution via locality-constrained low-rank representation[J]. IEEE Access, 2017, 5(99):13103-13117.
[4]	Kuang X D, Sui X B, Chen Q, et al. Single infrared image stripe noise removal using deep convolutional networks[J]. IEEE Journals Magazines, 2017, 9(1):1-13.
[5]	Dong C, Loy C L, He K, et al. Learning a deep convolutional network for image super-resolution[C]//European Conference on Computer Vision, 2014, 8692:184-199.
[6]	Xiao J, Hu R, Liao L, et al. Knowledge-based coding of objects for multisource surveillance video data[J]. IEEE Transactions on Multimedia, 2016, 18(9):1691-1706.
[7]	Jiang N, Yang Y, Hostmadsen A, et al. On the minimum energy of sending correlated sources over the Gaussian MAC[J]. IEEE Transactions on Information Theory, 2014, 60(10):6254-6275.
[8]	Li H, Xu L, Liu G. Face hallucination via similarity constraints[J]. IEEE Signal Processing Letters, 2013, 20(1):19-22.
[9]	Gao G, Jing X Y, Huang P, et al. Locality constrained double low-rank representation for effective face hallucination[J]. IEEE Access, 2017, 4(99):8775-8786.
[10]	Liang Y, Wang J, Zhou S, et al. Incorporating image priors with deep convolutional neural networks for image super-resolution[J]. Neurocomputing, 2016, 194(1):340-347.
[11]	Hostmadsen A, Uppal M, Xiong Z. On outage capacity in the low power regime[J]. IEEE Transactions on Information Theory, 2012, 58(2):888-896.
[12]	Yang J, Wright J, Huang T S, et al. Image super-resolution via sparse representation[J]. IEEE Transactions on Image Processing, 2010, 19(11):2861-2873.
[13]	Chang Y, Xia J, Gateno J, et al. An automatic and robust algorithm of re-establishment of digital dental occlusion[J]. IEEE Transactions on Medical Imaging, 2010, 29(9):1652-1663.
[14]	Zhang R, Zhao C, Xiong Z, et al. Pathway bridge based multi-objective optimization approach for lurking pathway prediction[J]. Bio Med Research International, 2014, 2014(1):351095.
[15]	Wu D, Liu T, Yang C, et al. Feasibility conditions for interference neutralization in relay-aided interference channel[J]. IEEE Transactions on Signal Processing, 2014, 62(6):1408-1423.

Nonlinear distortion image correction from confocal microscope based on interpolation

doi: 10.3788/IRLA201746.1103006

Abstract

References

Proportional views

通讯作者: 陈斌, bchen63@163.com

Article Metrics

Related

Proportional views