RGB-Guided Resolution Enhancement of IR Images Article Swipe
Marcel Trammer
,
Nils Genser
,
Jürgen Seiler
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1109/iwssip58668.2023.10180299
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1109/iwssip58668.2023.10180299
This paper introduces a novel method for RGB-Guided Resolution Enhancement of\ninfrared (IR) images called Guided IR Resolution Enhancement (GIRRE). In the\narea of single image super resolution (SISR) there exists a wide variety of\nalgorithms like interpolation methods or neural networks to improve the spatial\nresolution of images. In contrast to SISR, even more information can be\ngathered on the recorded scene when using multiple cameras. In our setup, we\nare dealing with multi image super resolution, especially with stereo super\nresolution. We consider a color camera and an IR camera. Current IR sensors\nhave a very low resolution compared to color sensors so that recent color\nsensors take up 100 times more pixels than IR sensors. To this end, GIRRE\nincreases the spatial resolution of the low-resolution IR image. After that,\nthe upscaled image is filtered with the aid of the high-resolution color image.\nWe show that our method achieves an average PSNR gain of 1.2 dB and at best up\nto 1.8 dB compared to state-of-the-art methods, which is visually noticeable.\n
Related Topics
Concepts
Image resolution
Artificial intelligence
Computer vision
RGB color model
Computer science
Pixel
Interpolation (computer graphics)
Resolution (logic)
Color image
Sub-pixel resolution
Image sensor
Contrast (vision)
Image (mathematics)
Image processing
Digital image processing
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1109/iwssip58668.2023.10180299
- OA Status
- green
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4384702804
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4384702804Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/iwssip58668.2023.10180299Digital Object Identifier
- Title
-
RGB-Guided Resolution Enhancement of IR ImagesWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-06-27Full publication date if available
- Authors
-
Marcel Trammer, Nils Genser, Jürgen SeilerList of authors in order
- Landing page
-
https://doi.org/10.1109/iwssip58668.2023.10180299Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2309.05996Direct OA link when available
- Concepts
-
Image resolution, Artificial intelligence, Computer vision, RGB color model, Computer science, Pixel, Interpolation (computer graphics), Resolution (logic), Color image, Sub-pixel resolution, Image sensor, Contrast (vision), Image (mathematics), Image processing, Digital image processingTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
25Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4384702804 |
|---|---|
| doi | https://doi.org/10.1109/iwssip58668.2023.10180299 |
| ids.doi | https://doi.org/10.1109/iwssip58668.2023.10180299 |
| ids.openalex | https://openalex.org/W4384702804 |
| fwci | 0.0 |
| type | preprint |
| title | RGB-Guided Resolution Enhancement of IR Images |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | 5 |
| biblio.first_page | 1 |
| topics[0].id | https://openalex.org/T11105 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 1.0 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1707 |
| topics[0].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[0].display_name | Advanced Image Processing Techniques |
| topics[1].id | https://openalex.org/T11019 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9988999962806702 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1707 |
| topics[1].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[1].display_name | Image Enhancement Techniques |
| topics[2].id | https://openalex.org/T10531 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9988999962806702 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1707 |
| topics[2].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[2].display_name | Advanced Vision and Imaging |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C205372480 |
| concepts[0].level | 2 |
| concepts[0].score | 0.7828997373580933 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q210521 |
| concepts[0].display_name | Image resolution |
| concepts[1].id | https://openalex.org/C154945302 |
| concepts[1].level | 1 |
| concepts[1].score | 0.7815216779708862 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[1].display_name | Artificial intelligence |
| concepts[2].id | https://openalex.org/C31972630 |
| concepts[2].level | 1 |
| concepts[2].score | 0.7347736358642578 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q844240 |
| concepts[2].display_name | Computer vision |
| concepts[3].id | https://openalex.org/C82990744 |
| concepts[3].level | 2 |
| concepts[3].score | 0.7203309535980225 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q166194 |
| concepts[3].display_name | RGB color model |
| concepts[4].id | https://openalex.org/C41008148 |
| concepts[4].level | 0 |
| concepts[4].score | 0.680361807346344 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[4].display_name | Computer science |
| concepts[5].id | https://openalex.org/C160633673 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5786758065223694 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q355198 |
| concepts[5].display_name | Pixel |
| concepts[6].id | https://openalex.org/C137800194 |
| concepts[6].level | 3 |
| concepts[6].score | 0.5124812126159668 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q11713455 |
| concepts[6].display_name | Interpolation (computer graphics) |
| concepts[7].id | https://openalex.org/C138268822 |
| concepts[7].level | 2 |
| concepts[7].score | 0.5087442994117737 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q1051925 |
| concepts[7].display_name | Resolution (logic) |
| concepts[8].id | https://openalex.org/C142616399 |
| concepts[8].level | 4 |
| concepts[8].score | 0.48938411474227905 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q5148604 |
| concepts[8].display_name | Color image |
| concepts[9].id | https://openalex.org/C152363868 |
| concepts[9].level | 5 |
| concepts[9].score | 0.4735545217990875 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q7630627 |
| concepts[9].display_name | Sub-pixel resolution |
| concepts[10].id | https://openalex.org/C76935873 |
| concepts[10].level | 2 |
| concepts[10].score | 0.42772701382637024 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q209121 |
| concepts[10].display_name | Image sensor |
| concepts[11].id | https://openalex.org/C2776502983 |
| concepts[11].level | 2 |
| concepts[11].score | 0.41492295265197754 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q690182 |
| concepts[11].display_name | Contrast (vision) |
| concepts[12].id | https://openalex.org/C115961682 |
| concepts[12].level | 2 |
| concepts[12].score | 0.3788490891456604 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q860623 |
| concepts[12].display_name | Image (mathematics) |
| concepts[13].id | https://openalex.org/C9417928 |
| concepts[13].level | 3 |
| concepts[13].score | 0.2638857066631317 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q1070689 |
| concepts[13].display_name | Image processing |
| concepts[14].id | https://openalex.org/C104317675 |
| concepts[14].level | 4 |
| concepts[14].score | 0.16634348034858704 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q1070689 |
| concepts[14].display_name | Digital image processing |
| keywords[0].id | https://openalex.org/keywords/image-resolution |
| keywords[0].score | 0.7828997373580933 |
| keywords[0].display_name | Image resolution |
| keywords[1].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[1].score | 0.7815216779708862 |
| keywords[1].display_name | Artificial intelligence |
| keywords[2].id | https://openalex.org/keywords/computer-vision |
| keywords[2].score | 0.7347736358642578 |
| keywords[2].display_name | Computer vision |
| keywords[3].id | https://openalex.org/keywords/rgb-color-model |
| keywords[3].score | 0.7203309535980225 |
| keywords[3].display_name | RGB color model |
| keywords[4].id | https://openalex.org/keywords/computer-science |
| keywords[4].score | 0.680361807346344 |
| keywords[4].display_name | Computer science |
| keywords[5].id | https://openalex.org/keywords/pixel |
| keywords[5].score | 0.5786758065223694 |
| keywords[5].display_name | Pixel |
| keywords[6].id | https://openalex.org/keywords/interpolation |
| keywords[6].score | 0.5124812126159668 |
| keywords[6].display_name | Interpolation (computer graphics) |
| keywords[7].id | https://openalex.org/keywords/resolution |
| keywords[7].score | 0.5087442994117737 |
| keywords[7].display_name | Resolution (logic) |
| keywords[8].id | https://openalex.org/keywords/color-image |
| keywords[8].score | 0.48938411474227905 |
| keywords[8].display_name | Color image |
| keywords[9].id | https://openalex.org/keywords/sub-pixel-resolution |
| keywords[9].score | 0.4735545217990875 |
| keywords[9].display_name | Sub-pixel resolution |
| keywords[10].id | https://openalex.org/keywords/image-sensor |
| keywords[10].score | 0.42772701382637024 |
| keywords[10].display_name | Image sensor |
| keywords[11].id | https://openalex.org/keywords/contrast |
| keywords[11].score | 0.41492295265197754 |
| keywords[11].display_name | Contrast (vision) |
| keywords[12].id | https://openalex.org/keywords/image |
| keywords[12].score | 0.3788490891456604 |
| keywords[12].display_name | Image (mathematics) |
| keywords[13].id | https://openalex.org/keywords/image-processing |
| keywords[13].score | 0.2638857066631317 |
| keywords[13].display_name | Image processing |
| keywords[14].id | https://openalex.org/keywords/digital-image-processing |
| keywords[14].score | 0.16634348034858704 |
| keywords[14].display_name | Digital image processing |
| language | en |
| locations[0].id | doi:10.1109/iwssip58668.2023.10180299 |
| locations[0].is_oa | False |
| locations[0].source | |
| locations[0].license | |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | proceedings-article |
| locations[0].license_id | |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | 2023 30th International Conference on Systems, Signals and Image Processing (IWSSIP) |
| locations[0].landing_page_url | https://doi.org/10.1109/iwssip58668.2023.10180299 |
| locations[1].id | pmh:oai:arXiv.org:2309.05996 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400194 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | True |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | arXiv (Cornell University) |
| locations[1].source.host_organization | https://openalex.org/I205783295 |
| locations[1].source.host_organization_name | Cornell University |
| locations[1].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[1].license | |
| locations[1].pdf_url | https://arxiv.org/pdf/2309.05996 |
| locations[1].version | submittedVersion |
| locations[1].raw_type | text |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | http://arxiv.org/abs/2309.05996 |
| indexed_in | arxiv, crossref |
| authorships[0].author.id | https://openalex.org/A5092495533 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Marcel Trammer |
| authorships[0].affiliations[0].raw_affiliation_string | AI & Signal Processing Lab, Sielaff GmbH & Co. KG,Herrieden,Germany |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Marcel Trammer |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | AI & Signal Processing Lab, Sielaff GmbH & Co. KG,Herrieden,Germany |
| authorships[1].author.id | https://openalex.org/A5052676758 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-8564-2541 |
| authorships[1].author.display_name | Nils Genser |
| authorships[1].affiliations[0].raw_affiliation_string | AI & Signal Processing Lab, Sielaff GmbH & Co. KG,Herrieden,Germany |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Nils Genser |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | AI & Signal Processing Lab, Sielaff GmbH & Co. KG,Herrieden,Germany |
| authorships[2].author.id | https://openalex.org/A5034495569 |
| authorships[2].author.orcid | https://orcid.org/0000-0001-5295-893X |
| authorships[2].author.display_name | Jürgen Seiler |
| authorships[2].countries | DE |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I181369854 |
| authorships[2].affiliations[0].raw_affiliation_string | Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU),Multimedia Communications and Signal Processing,Erlangen,Germany |
| authorships[2].institutions[0].id | https://openalex.org/I181369854 |
| authorships[2].institutions[0].ror | https://ror.org/00f7hpc57 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I181369854 |
| authorships[2].institutions[0].country_code | DE |
| authorships[2].institutions[0].display_name | Friedrich-Alexander-Universität Erlangen-Nürnberg |
| authorships[2].author_position | last |
| authorships[2].raw_author_name | Jürgen Seiler |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU),Multimedia Communications and Signal Processing,Erlangen,Germany |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/2309.05996 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | RGB-Guided Resolution Enhancement of IR Images |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11105 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 1.0 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1707 |
| primary_topic.subfield.display_name | Computer Vision and Pattern Recognition |
| primary_topic.display_name | Advanced Image Processing Techniques |
| related_works | https://openalex.org/W1994545243, https://openalex.org/W2025530126, https://openalex.org/W2216907995, https://openalex.org/W1986138551, https://openalex.org/W2350512350, https://openalex.org/W2113253671, https://openalex.org/W2086128134, https://openalex.org/W2135032339, https://openalex.org/W2688472305, https://openalex.org/W2120771670 |
| cited_by_count | 0 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:2309.05996 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://arxiv.org/pdf/2309.05996 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | text |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/2309.05996 |
| primary_location.id | doi:10.1109/iwssip58668.2023.10180299 |
| primary_location.is_oa | False |
| primary_location.source | |
| primary_location.license | |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | proceedings-article |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | 2023 30th International Conference on Systems, Signals and Image Processing (IWSSIP) |
| primary_location.landing_page_url | https://doi.org/10.1109/iwssip58668.2023.10180299 |
| publication_date | 2023-06-27 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W3089096897, https://openalex.org/W2798881637, https://openalex.org/W3090937333, https://openalex.org/W2087380704, https://openalex.org/W2097615965, https://openalex.org/W2242218935, https://openalex.org/W4312744028, https://openalex.org/W2963372104, https://openalex.org/W4323022426, https://openalex.org/W2639036976, https://openalex.org/W2964040059, https://openalex.org/W2922435819, https://openalex.org/W3013529009, https://openalex.org/W1913633284, https://openalex.org/W2295844283, https://openalex.org/W2083478619, https://openalex.org/W2125188192, https://openalex.org/W2787894218, https://openalex.org/W2502759836, https://openalex.org/W2219845087, https://openalex.org/W2900956758, https://openalex.org/W2100109944, https://openalex.org/W2012946078, https://openalex.org/W2897015430, https://openalex.org/W3130927609 |
| referenced_works_count | 25 |
| abstract_inverted_index.a | 3, 29, 78, 88 |
| abstract_inverted_index.IR | 15, 83, 86, 107, 119 |
| abstract_inverted_index.In | 19, 45, 62 |
| abstract_inverted_index.To | 109 |
| abstract_inverted_index.We | 76 |
| abstract_inverted_index.an | 82, 140 |
| abstract_inverted_index.at | 148 |
| abstract_inverted_index.dB | 146, 152 |
| abstract_inverted_index.is | 125, 158 |
| abstract_inverted_index.of | 21, 43, 116, 130, 144 |
| abstract_inverted_index.on | 54 |
| abstract_inverted_index.or | 36 |
| abstract_inverted_index.so | 96 |
| abstract_inverted_index.to | 39, 47, 93, 154 |
| abstract_inverted_index.up | 101 |
| abstract_inverted_index.1.2 | 145 |
| abstract_inverted_index.1.8 | 151 |
| abstract_inverted_index.100 | 102 |
| abstract_inverted_index.aid | 129 |
| abstract_inverted_index.and | 81, 147 |
| abstract_inverted_index.can | 52 |
| abstract_inverted_index.for | 6 |
| abstract_inverted_index.low | 90 |
| abstract_inverted_index.our | 63, 137 |
| abstract_inverted_index.the | 41, 55, 113, 117, 128, 131 |
| abstract_inverted_index.(IR) | 11 |
| abstract_inverted_index.PSNR | 142 |
| abstract_inverted_index.This | 0 |
| abstract_inverted_index.best | 149 |
| abstract_inverted_index.end, | 111 |
| abstract_inverted_index.even | 49 |
| abstract_inverted_index.gain | 143 |
| abstract_inverted_index.like | 33 |
| abstract_inverted_index.more | 50, 104 |
| abstract_inverted_index.show | 135 |
| abstract_inverted_index.take | 100 |
| abstract_inverted_index.than | 106 |
| abstract_inverted_index.that | 97, 136 |
| abstract_inverted_index.this | 110 |
| abstract_inverted_index.very | 89 |
| abstract_inverted_index.when | 58 |
| abstract_inverted_index.wide | 30 |
| abstract_inverted_index.with | 67, 73, 127 |
| abstract_inverted_index.After | 121 |
| abstract_inverted_index.SISR, | 48 |
| abstract_inverted_index.color | 79, 94, 133 |
| abstract_inverted_index.image | 23, 69, 124 |
| abstract_inverted_index.multi | 68 |
| abstract_inverted_index.novel | 4 |
| abstract_inverted_index.paper | 1 |
| abstract_inverted_index.scene | 57 |
| abstract_inverted_index.super | 24, 70 |
| abstract_inverted_index.there | 27 |
| abstract_inverted_index.times | 103 |
| abstract_inverted_index.using | 59 |
| abstract_inverted_index.which | 157 |
| abstract_inverted_index.(SISR) | 26 |
| abstract_inverted_index.Guided | 14 |
| abstract_inverted_index.called | 13 |
| abstract_inverted_index.camera | 80 |
| abstract_inverted_index.exists | 28 |
| abstract_inverted_index.image. | 120 |
| abstract_inverted_index.images | 12 |
| abstract_inverted_index.method | 5, 138 |
| abstract_inverted_index.neural | 37 |
| abstract_inverted_index.pixels | 105 |
| abstract_inverted_index.recent | 98 |
| abstract_inverted_index.setup, | 64 |
| abstract_inverted_index.single | 22 |
| abstract_inverted_index.stereo | 74 |
| abstract_inverted_index.up\nto | 150 |
| abstract_inverted_index.Current | 85 |
| abstract_inverted_index.average | 141 |
| abstract_inverted_index.camera. | 84 |
| abstract_inverted_index.dealing | 66 |
| abstract_inverted_index.images. | 44 |
| abstract_inverted_index.improve | 40 |
| abstract_inverted_index.methods | 35 |
| abstract_inverted_index.sensors | 95 |
| abstract_inverted_index.spatial | 114 |
| abstract_inverted_index.variety | 31 |
| abstract_inverted_index.we\nare | 65 |
| abstract_inverted_index.(GIRRE). | 18 |
| abstract_inverted_index.achieves | 139 |
| abstract_inverted_index.cameras. | 61 |
| abstract_inverted_index.compared | 92, 153 |
| abstract_inverted_index.consider | 77 |
| abstract_inverted_index.contrast | 46 |
| abstract_inverted_index.filtered | 126 |
| abstract_inverted_index.methods, | 156 |
| abstract_inverted_index.multiple | 60 |
| abstract_inverted_index.networks | 38 |
| abstract_inverted_index.recorded | 56 |
| abstract_inverted_index.sensors. | 108 |
| abstract_inverted_index.upscaled | 123 |
| abstract_inverted_index.visually | 159 |
| abstract_inverted_index.the\narea | 20 |
| abstract_inverted_index.RGB-Guided | 7 |
| abstract_inverted_index.Resolution | 8, 16 |
| abstract_inverted_index.especially | 72 |
| abstract_inverted_index.image.\nWe | 134 |
| abstract_inverted_index.introduces | 2 |
| abstract_inverted_index.resolution | 25, 91, 115 |
| abstract_inverted_index.that,\nthe | 122 |
| abstract_inverted_index.Enhancement | 9, 17 |
| abstract_inverted_index.information | 51 |
| abstract_inverted_index.resolution, | 71 |
| abstract_inverted_index.be\ngathered | 53 |
| abstract_inverted_index.of\ninfrared | 10 |
| abstract_inverted_index.interpolation | 34 |
| abstract_inverted_index.noticeable.\n | 160 |
| abstract_inverted_index.sensors\nhave | 87 |
| abstract_inverted_index.color\nsensors | 99 |
| abstract_inverted_index.low-resolution | 118 |
| abstract_inverted_index.of\nalgorithms | 32 |
| abstract_inverted_index.high-resolution | 132 |
| abstract_inverted_index.GIRRE\nincreases | 112 |
| abstract_inverted_index.state-of-the-art | 155 |
| abstract_inverted_index.super\nresolution. | 75 |
| abstract_inverted_index.spatial\nresolution | 42 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.0883367 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |