| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_e488ab91efcae5e08ec613a789056da0 |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-20081
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-10076
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-10016 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T5-002
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T3-4076
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T3-4053
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06K9-6247
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06K9-6256
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-2135
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V10-30
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-214
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V10-52
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V10-772
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V20-46 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T3-40
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06K9-62
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T5-00 |
| filingDate |
2021-05-28-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_21ef95b74839ab77d493e58e63a167ac |
| publicationDate |
2021-09-30-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
US-2021304360-A1 |
| titleOfInvention |
Image upsampling and denoising by learning pairs of low-resolution dictionaries using a structured subspace model |
| abstract |
A computational method is disclosed for producing a sequence of high-resolution (HR) images from an input sequence of low-resolution (LR) images. The method uses a structured subspace framework to learn pairs of LR dictionaries from the input LR sequence ‘and’ employ learned pairs of LR dictionaries into estimating HR images. The structured subspace framework itself is based on a pair of specially structured HR basis matrices, wherein a HR basis spans any HR image whose so-called polyphase components (PPCs) are spanned by the corresponding LR dictionary. The computational method may be used to denoise images, whether LR or HR images, by using the structured subspace framework to learn dictionaries of the images and estimate a denoised version of the images from the learned image dictionaries. The denoising process may be iterated until the noise is reduced below a desired threshold. |
| priorityDate |
2019-07-16-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |