Resolution-Enhancement for an Integral Imaging Microscopy Using Deep Learning

被引：12

作者：

Kwon, Ki-Chul ^{[1
]}

Kwon, Ki Hoon ^{[2
]}

Erdenebat, Munkh-Uchral ^{[1
]}

Piao, Yan-Ling ^{[1
]}

Lim, Young-Tae ^{[1
]}

Kim, Min Young ^{[2
,3
]}

Kim, Nam ^{[1
]}

机构：

[1] Chungbuk Natl Univ, Sch Informat & Commun Engn, Chungbuk 28644, South Korea

[2] Kyungpook Natl Univ, Sch Elect Engn, Daegu 41566, South Korea

[3] Kyungpook Natl Univ, Res Ctr Neurosurg Robot Syst, Daegu 41566, South Korea

来源：

IEEE PHOTONICS JOURNAL | 2019年 / 11卷 / 01期

基金：

新加坡国家研究基金会;

关键词：

Deep learning; integral imaging microscopy; resolution enhancement; DEPTH-OF-FIELD; DISPLAY; SYSTEM;

D O I：

10.1109/JPHOT.2018.2890429

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

A novel resolution-enhancement method for an integral imaging microscopy that applies interpolation and deep learning is proposed, and the complete system with both hardware and software components is implemented. The resolution of the captured elemental image array is increased by generating intermediate-view elemental images between each neighboring elemental image, and an orthographic-view visualization of the specimen is reconstructed. Then, a deep learning algorithm is used to generate maximum possible resolution for each reconstructed directional-view image with improved quality. Since a pre-trained model is applied, the proposed system processes the images directly without data training. The experimental results indicate that the proposed system produces resolution-enhanced directional-view images, and quantitative evaluation methods for reconstructed images such as the peak signal-to-noise ratio and the power spectral density confirm that the proposed system provides improvements in image quality.

引用

页数：12

共 24 条

[1] Image Super-Resolution Using Deep Convolutional Networks [J].