Super-Resolution and Large Depth of Field Model for Optical Microscope Imaging
Due to the limitation of numerical aperture (NA) in a microscope, it is very difficult to obtain a clear image of the specimen with a large depth of field (DOF). We propose a deep learning network model to simultaneously improve the imaging resolution and DOF of optical microscopes. The proposed M-D...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | International Journal of Optics |
| Online Access: | http://dx.doi.org/10.1155/2021/6493130 |
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| author | Ruo-Peng Zheng Shu-Bin Liu Lei Li |
| author_facet | Ruo-Peng Zheng Shu-Bin Liu Lei Li |
| author_sort | Ruo-Peng Zheng |
| collection | DOAJ |
| description | Due to the limitation of numerical aperture (NA) in a microscope, it is very difficult to obtain a clear image of the specimen with a large depth of field (DOF). We propose a deep learning network model to simultaneously improve the imaging resolution and DOF of optical microscopes. The proposed M-Deblurgan consists of three parts: (i) a deblurring module equipped with an encoder-decoder network for feature extraction, (ii) an optimal approximation module to reduce the error propagation between the two tasks, and (iii) an SR module to super-resolve the image from the output of the optimal approximation module. The experimental results show that the proposed network model reaches the optimal result. The peak signal-to-noise ratio (PSNR) of the method can reach 37.5326, and the structural similarity (SSIM) can reach 0.9551 in the experimental dataset. The method can also be used in other potential applications, such as microscopes, mobile cameras, and telescopes. |
| format | Article |
| id | doaj-art-84fc3249948b4076b8006728f75bad88 |
| institution | Kabale University |
| issn | 1687-9392 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Optics |
| spelling | doaj-art-84fc3249948b4076b8006728f75bad882025-02-03T01:04:12ZengWileyInternational Journal of Optics1687-93922021-01-01202110.1155/2021/6493130Super-Resolution and Large Depth of Field Model for Optical Microscope ImagingRuo-Peng Zheng0Shu-Bin Liu1Lei Li2School of Electronics and Information EngineeringSchool of Electronics and Information EngineeringSchool of Electronics and Information EngineeringDue to the limitation of numerical aperture (NA) in a microscope, it is very difficult to obtain a clear image of the specimen with a large depth of field (DOF). We propose a deep learning network model to simultaneously improve the imaging resolution and DOF of optical microscopes. The proposed M-Deblurgan consists of three parts: (i) a deblurring module equipped with an encoder-decoder network for feature extraction, (ii) an optimal approximation module to reduce the error propagation between the two tasks, and (iii) an SR module to super-resolve the image from the output of the optimal approximation module. The experimental results show that the proposed network model reaches the optimal result. The peak signal-to-noise ratio (PSNR) of the method can reach 37.5326, and the structural similarity (SSIM) can reach 0.9551 in the experimental dataset. The method can also be used in other potential applications, such as microscopes, mobile cameras, and telescopes.http://dx.doi.org/10.1155/2021/6493130 |
| spellingShingle | Ruo-Peng Zheng Shu-Bin Liu Lei Li Super-Resolution and Large Depth of Field Model for Optical Microscope Imaging International Journal of Optics |
| title | Super-Resolution and Large Depth of Field Model for Optical Microscope Imaging |
| title_full | Super-Resolution and Large Depth of Field Model for Optical Microscope Imaging |
| title_fullStr | Super-Resolution and Large Depth of Field Model for Optical Microscope Imaging |
| title_full_unstemmed | Super-Resolution and Large Depth of Field Model for Optical Microscope Imaging |
| title_short | Super-Resolution and Large Depth of Field Model for Optical Microscope Imaging |
| title_sort | super resolution and large depth of field model for optical microscope imaging |
| url | http://dx.doi.org/10.1155/2021/6493130 |
| work_keys_str_mv | AT ruopengzheng superresolutionandlargedepthoffieldmodelforopticalmicroscopeimaging AT shubinliu superresolutionandlargedepthoffieldmodelforopticalmicroscopeimaging AT leili superresolutionandlargedepthoffieldmodelforopticalmicroscopeimaging |