http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-114587272-A
Outgoing Links
| Predicate | Object |
|---|---|
| assignee | http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_5d58981fab4bd1d823e810c26413d484 |
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-0071 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-7267 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/A61B5-00 |
| filingDate | 2022-02-25-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor | http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_711fc4e24b637219ed95402dc3de9419 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_48ec5e90f640dc730792d6c0c9097049 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_9c64d37453dac5abc23054bbbc521632 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_4099ace36371dc618e67cbd3a7c8dbbb http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_74863f27b4627cd467084b62d686c33d http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_8960d19cc9bcabb4339495b1ae4f7022 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_d5c9a8eab30fa893a0a7323051fb7ae8 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_3922ad84ff961b77f9586c0ebe474928 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_b3fc60c72bfe29fc23e37ad9f6c33d9f |
| publicationDate | 2022-06-07-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | CN-114587272-A |
| titleOfInvention | A deep learning-based deblurring method for in vivo fluorescence imaging |
| abstract | The invention belongs to the technical field of fluorescence imaging, and discloses a deblurring method for in vivo fluorescence imaging based on deep learning. The infrared second-region fluorescence camera simultaneously collects the fluorescence images of mice; the collected live fluorescence images are randomly divided into training data sets, validation data sets and test data sets according to a certain proportion; the full gradient loss method is used to construct fluorescence images from the near-infrared first region. The generative adversarial network GAN, which transforms the image to the near-infrared second-region fluorescence image, uses the constructed generative adversarial network to train the collected live fluorescence images; the trained network is used to calculate the near-infrared region-1 fluorescence image to obtain the deblurred image. Fluorescence image. The deblurred fluorescent image of the present invention not only has the sensitivity of the near-infrared first-region fluorescence image, but also has the similar clarity of the near-infrared second-region fluorescence image. |
| priorityDate | 2022-02-25-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
Total number of triples: 24.