Local Excitation Network for Restoring a JPEG-Compressed Image Article Swipe
Songhyun Yu
,
Jechang Jeong
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1109/access.2019.2943155
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1109/access.2019.2943155
Joint photographic experts group (JPEG) compression is lossy compression, and degradation of image quality worsens at high compression ratios. Therefore, a reconstruction process is required for a visually pleasant image. In this paper, we propose an end-to-end deep learning architecture for restoring JPEG images with high compression ratios. The proposed architecture changes a core principle of the squeeze and excitation network for low-level vision tasks where pixel-level accuracy is important. Instead of extracting global features, our network extracts locally embedded features and fine-tunes each feature value by using depthwise convolution. To reduce the computational complexity and parameters with large receptive fields, we use a combination of the recursive structure and feature map down- and up-scaling processes. We also propose a compact version of the proposed model by decreasing the number of filters and simplifying the network, which has about one-twentieth of the parameters of the baseline model. Experimental results reveal that our network outperforms conventional networks quantitatively, and the restored images are clear with sharp edges and smooth blocking boundaries. Furthermore, the compact model shows higher objective results while maintaining a low number of parameters. In addition, at a high compression ratio, the overall information, including details in the blocks, are lost owing to high quantization errors. We apply a generative adversarial network structure to restore these highly damaged blocks, and the results reveal that the image produced has details similar to those of the ground truth.
Related Topics
Concepts
Computer science
JPEG
Compression artifact
Lossy compression
Quantization (signal processing)
Artificial intelligence
Image compression
JPEG 2000
Feature (linguistics)
Computer vision
Deblurring
Network architecture
Pattern recognition (psychology)
Data compression
Algorithm
Image restoration
Image (mathematics)
Image processing
Computer security
Linguistics
Philosophy
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2019.2943155
- https://ieeexplore.ieee.org/ielx7/6287639/8600701/08846754.pdf
- OA Status
- gold
- Cited By
- 5
- References
- 43
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2976437790
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2976437790Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/access.2019.2943155Digital Object Identifier
- Title
-
Local Excitation Network for Restoring a JPEG-Compressed ImageWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-01-01Full publication date if available
- Authors
-
Songhyun Yu, Jechang JeongList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2019.2943155Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/6287639/8600701/08846754.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://ieeexplore.ieee.org/ielx7/6287639/8600701/08846754.pdfDirect OA link when available
- Concepts
-
Computer science, JPEG, Compression artifact, Lossy compression, Quantization (signal processing), Artificial intelligence, Image compression, JPEG 2000, Feature (linguistics), Computer vision, Deblurring, Network architecture, Pattern recognition (psychology), Data compression, Algorithm, Image restoration, Image (mathematics), Image processing, Computer security, Linguistics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
5Total citation count in OpenAlex
- Citations by year (recent)
-
2022: 1, 2020: 4Per-year citation counts (last 5 years)
- References (count)
-
43Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W2976437790 |
|---|---|
| doi | https://doi.org/10.1109/access.2019.2943155 |
| ids.doi | https://doi.org/10.1109/access.2019.2943155 |
| ids.mag | 2976437790 |
| ids.openalex | https://openalex.org/W2976437790 |
| fwci | 0.53442879 |
| type | article |
| title | Local Excitation Network for Restoring a JPEG-Compressed Image |
| biblio.issue | |
| biblio.volume | 7 |
| biblio.last_page | 138042 |
| biblio.first_page | 138032 |
| 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 | 0.9998000264167786 |
| 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/T10688 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9987000226974487 |
| 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 and Signal Denoising Methods |
| topics[2].id | https://openalex.org/T12357 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9969000220298767 |
| 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 | Digital Media Forensic Detection |
| funders[0].id | https://openalex.org/F4320323103 |
| funders[0].ror | https://ror.org/05fhe0r85 |
| funders[0].display_name | Agency for Defense Development |
| funders[1].id | https://openalex.org/F4320334874 |
| funders[1].ror | https://ror.org/04bjg9m96 |
| funders[1].display_name | Defense Acquisition Program Administration |
| is_xpac | False |
| apc_list.value | 1850 |
| apc_list.currency | USD |
| apc_list.value_usd | 1850 |
| apc_paid.value | 1850 |
| apc_paid.currency | USD |
| apc_paid.value_usd | 1850 |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.7746541500091553 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C198751489 |
| concepts[1].level | 3 |
| concepts[1].score | 0.6654939651489258 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q2195 |
| concepts[1].display_name | JPEG |
| concepts[2].id | https://openalex.org/C57654395 |
| concepts[2].level | 5 |
| concepts[2].score | 0.6308397054672241 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q1097775 |
| concepts[2].display_name | Compression artifact |
| concepts[3].id | https://openalex.org/C165021410 |
| concepts[3].level | 2 |
| concepts[3].score | 0.624918520450592 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q55564 |
| concepts[3].display_name | Lossy compression |
| concepts[4].id | https://openalex.org/C28855332 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5790671110153198 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q198099 |
| concepts[4].display_name | Quantization (signal processing) |
| concepts[5].id | https://openalex.org/C154945302 |
| concepts[5].level | 1 |
| concepts[5].score | 0.5771470069885254 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[5].display_name | Artificial intelligence |
| concepts[6].id | https://openalex.org/C13481523 |
| concepts[6].level | 4 |
| concepts[6].score | 0.5640116930007935 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q412438 |
| concepts[6].display_name | Image compression |
| concepts[7].id | https://openalex.org/C69216139 |
| concepts[7].level | 5 |
| concepts[7].score | 0.4777342677116394 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q931783 |
| concepts[7].display_name | JPEG 2000 |
| concepts[8].id | https://openalex.org/C2776401178 |
| concepts[8].level | 2 |
| concepts[8].score | 0.45155489444732666 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q12050496 |
| concepts[8].display_name | Feature (linguistics) |
| concepts[9].id | https://openalex.org/C31972630 |
| concepts[9].level | 1 |
| concepts[9].score | 0.4513111710548401 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q844240 |
| concepts[9].display_name | Computer vision |
| concepts[10].id | https://openalex.org/C2777693668 |
| concepts[10].level | 5 |
| concepts[10].score | 0.4472384452819824 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q25053743 |
| concepts[10].display_name | Deblurring |
| concepts[11].id | https://openalex.org/C193415008 |
| concepts[11].level | 2 |
| concepts[11].score | 0.42754796147346497 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q639681 |
| concepts[11].display_name | Network architecture |
| concepts[12].id | https://openalex.org/C153180895 |
| concepts[12].level | 2 |
| concepts[12].score | 0.36862674355506897 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[12].display_name | Pattern recognition (psychology) |
| concepts[13].id | https://openalex.org/C78548338 |
| concepts[13].level | 2 |
| concepts[13].score | 0.36114975810050964 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q2493 |
| concepts[13].display_name | Data compression |
| concepts[14].id | https://openalex.org/C11413529 |
| concepts[14].level | 1 |
| concepts[14].score | 0.35004115104675293 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[14].display_name | Algorithm |
| concepts[15].id | https://openalex.org/C106430172 |
| concepts[15].level | 4 |
| concepts[15].score | 0.2886345088481903 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q6002272 |
| concepts[15].display_name | Image restoration |
| concepts[16].id | https://openalex.org/C115961682 |
| concepts[16].level | 2 |
| concepts[16].score | 0.2625512480735779 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q860623 |
| concepts[16].display_name | Image (mathematics) |
| concepts[17].id | https://openalex.org/C9417928 |
| concepts[17].level | 3 |
| concepts[17].score | 0.25413060188293457 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q1070689 |
| concepts[17].display_name | Image processing |
| concepts[18].id | https://openalex.org/C38652104 |
| concepts[18].level | 1 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q3510521 |
| concepts[18].display_name | Computer security |
| concepts[19].id | https://openalex.org/C41895202 |
| concepts[19].level | 1 |
| concepts[19].score | 0.0 |
| concepts[19].wikidata | https://www.wikidata.org/wiki/Q8162 |
| concepts[19].display_name | Linguistics |
| concepts[20].id | https://openalex.org/C138885662 |
| concepts[20].level | 0 |
| concepts[20].score | 0.0 |
| concepts[20].wikidata | https://www.wikidata.org/wiki/Q5891 |
| concepts[20].display_name | Philosophy |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.7746541500091553 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/jpeg |
| keywords[1].score | 0.6654939651489258 |
| keywords[1].display_name | JPEG |
| keywords[2].id | https://openalex.org/keywords/compression-artifact |
| keywords[2].score | 0.6308397054672241 |
| keywords[2].display_name | Compression artifact |
| keywords[3].id | https://openalex.org/keywords/lossy-compression |
| keywords[3].score | 0.624918520450592 |
| keywords[3].display_name | Lossy compression |
| keywords[4].id | https://openalex.org/keywords/quantization |
| keywords[4].score | 0.5790671110153198 |
| keywords[4].display_name | Quantization (signal processing) |
| keywords[5].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[5].score | 0.5771470069885254 |
| keywords[5].display_name | Artificial intelligence |
| keywords[6].id | https://openalex.org/keywords/image-compression |
| keywords[6].score | 0.5640116930007935 |
| keywords[6].display_name | Image compression |
| keywords[7].id | https://openalex.org/keywords/jpeg-2000 |
| keywords[7].score | 0.4777342677116394 |
| keywords[7].display_name | JPEG 2000 |
| keywords[8].id | https://openalex.org/keywords/feature |
| keywords[8].score | 0.45155489444732666 |
| keywords[8].display_name | Feature (linguistics) |
| keywords[9].id | https://openalex.org/keywords/computer-vision |
| keywords[9].score | 0.4513111710548401 |
| keywords[9].display_name | Computer vision |
| keywords[10].id | https://openalex.org/keywords/deblurring |
| keywords[10].score | 0.4472384452819824 |
| keywords[10].display_name | Deblurring |
| keywords[11].id | https://openalex.org/keywords/network-architecture |
| keywords[11].score | 0.42754796147346497 |
| keywords[11].display_name | Network architecture |
| keywords[12].id | https://openalex.org/keywords/pattern-recognition |
| keywords[12].score | 0.36862674355506897 |
| keywords[12].display_name | Pattern recognition (psychology) |
| keywords[13].id | https://openalex.org/keywords/data-compression |
| keywords[13].score | 0.36114975810050964 |
| keywords[13].display_name | Data compression |
| keywords[14].id | https://openalex.org/keywords/algorithm |
| keywords[14].score | 0.35004115104675293 |
| keywords[14].display_name | Algorithm |
| keywords[15].id | https://openalex.org/keywords/image-restoration |
| keywords[15].score | 0.2886345088481903 |
| keywords[15].display_name | Image restoration |
| keywords[16].id | https://openalex.org/keywords/image |
| keywords[16].score | 0.2625512480735779 |
| keywords[16].display_name | Image (mathematics) |
| keywords[17].id | https://openalex.org/keywords/image-processing |
| keywords[17].score | 0.25413060188293457 |
| keywords[17].display_name | Image processing |
| language | en |
| locations[0].id | doi:10.1109/access.2019.2943155 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S2485537415 |
| locations[0].source.issn | 2169-3536 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2169-3536 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | IEEE Access |
| locations[0].source.host_organization | https://openalex.org/P4310319808 |
| locations[0].source.host_organization_name | Institute of Electrical and Electronics Engineers |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310319808 |
| locations[0].source.host_organization_lineage_names | Institute of Electrical and Electronics Engineers |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://ieeexplore.ieee.org/ielx7/6287639/8600701/08846754.pdf |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | IEEE Access |
| locations[0].landing_page_url | https://doi.org/10.1109/access.2019.2943155 |
| locations[1].id | pmh:oai:doaj.org/article:de8ac067bd674cda936191b428943195 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306401280 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[1].source.host_organization | |
| locations[1].source.host_organization_name | |
| locations[1].license | cc-by-sa |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | article |
| locations[1].license_id | https://openalex.org/licenses/cc-by-sa |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | IEEE Access, Vol 7, Pp 138032-138042 (2019) |
| locations[1].landing_page_url | https://doaj.org/article/de8ac067bd674cda936191b428943195 |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5001242905 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Songhyun Yu |
| authorships[0].countries | KR |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I4575257 |
| authorships[0].affiliations[0].raw_affiliation_string | Department of Electronics and Computer Engineering, Hanyang University, Seoul, South Korea |
| authorships[0].institutions[0].id | https://openalex.org/I4575257 |
| authorships[0].institutions[0].ror | https://ror.org/046865y68 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I4575257 |
| authorships[0].institutions[0].country_code | KR |
| authorships[0].institutions[0].display_name | Hanyang University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Songhyun Yu |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Department of Electronics and Computer Engineering, Hanyang University, Seoul, South Korea |
| authorships[1].author.id | https://openalex.org/A5018642097 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-3759-3116 |
| authorships[1].author.display_name | Jechang Jeong |
| authorships[1].countries | KR |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I4575257 |
| authorships[1].affiliations[0].raw_affiliation_string | Department of Electronics and Computer Engineering, Hanyang University, Seoul, South Korea |
| authorships[1].institutions[0].id | https://openalex.org/I4575257 |
| authorships[1].institutions[0].ror | https://ror.org/046865y68 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I4575257 |
| authorships[1].institutions[0].country_code | KR |
| authorships[1].institutions[0].display_name | Hanyang University |
| authorships[1].author_position | last |
| authorships[1].raw_author_name | Jechang Jeong |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Department of Electronics and Computer Engineering, Hanyang University, Seoul, South Korea |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://ieeexplore.ieee.org/ielx7/6287639/8600701/08846754.pdf |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Local Excitation Network for Restoring a JPEG-Compressed Image |
| has_fulltext | True |
| 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 | 0.9998000264167786 |
| 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/W2441603424, https://openalex.org/W2542654834, https://openalex.org/W4243959093, https://openalex.org/W2971784143, https://openalex.org/W2159814397, https://openalex.org/W3095399624, https://openalex.org/W1968318766, https://openalex.org/W1994319845, https://openalex.org/W3137515556, https://openalex.org/W1979811710 |
| cited_by_count | 5 |
| counts_by_year[0].year | 2022 |
| counts_by_year[0].cited_by_count | 1 |
| counts_by_year[1].year | 2020 |
| counts_by_year[1].cited_by_count | 4 |
| locations_count | 2 |
| best_oa_location.id | doi:10.1109/access.2019.2943155 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S2485537415 |
| best_oa_location.source.issn | 2169-3536 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2169-3536 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | IEEE Access |
| best_oa_location.source.host_organization | https://openalex.org/P4310319808 |
| best_oa_location.source.host_organization_name | Institute of Electrical and Electronics Engineers |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310319808 |
| best_oa_location.source.host_organization_lineage_names | Institute of Electrical and Electronics Engineers |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://ieeexplore.ieee.org/ielx7/6287639/8600701/08846754.pdf |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | IEEE Access |
| best_oa_location.landing_page_url | https://doi.org/10.1109/access.2019.2943155 |
| primary_location.id | doi:10.1109/access.2019.2943155 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S2485537415 |
| primary_location.source.issn | 2169-3536 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2169-3536 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | IEEE Access |
| primary_location.source.host_organization | https://openalex.org/P4310319808 |
| primary_location.source.host_organization_name | Institute of Electrical and Electronics Engineers |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310319808 |
| primary_location.source.host_organization_lineage_names | Institute of Electrical and Electronics Engineers |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://ieeexplore.ieee.org/ielx7/6287639/8600701/08846754.pdf |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | IEEE Access |
| primary_location.landing_page_url | https://doi.org/10.1109/access.2019.2943155 |
| publication_date | 2019-01-01 |
| publication_year | 2019 |
| referenced_works | https://openalex.org/W6678461930, https://openalex.org/W2739757502, https://openalex.org/W2562637781, https://openalex.org/W1901129140, https://openalex.org/W1930824406, https://openalex.org/W2466611277, https://openalex.org/W6637373629, https://openalex.org/W2194775991, https://openalex.org/W1885185971, https://openalex.org/W2242218935, https://openalex.org/W2964125708, https://openalex.org/W2509784253, https://openalex.org/W2548527721, https://openalex.org/W2779176852, https://openalex.org/W2142683286, https://openalex.org/W1677182931, https://openalex.org/W2113641518, https://openalex.org/W2963966343, https://openalex.org/W2049340374, https://openalex.org/W1946766895, https://openalex.org/W6720887540, https://openalex.org/W2170829077, https://openalex.org/W1571378769, https://openalex.org/W2119557062, https://openalex.org/W1965077907, https://openalex.org/W1588663000, https://openalex.org/W2122086266, https://openalex.org/W6704408313, https://openalex.org/W2963470893, https://openalex.org/W2508457857, https://openalex.org/W2214802144, https://openalex.org/W6737664043, https://openalex.org/W2752782242, https://openalex.org/W2476548250, https://openalex.org/W2121927366, https://openalex.org/W2962835968, https://openalex.org/W2963420686, https://openalex.org/W2345337169, https://openalex.org/W2612445135, https://openalex.org/W4320013936, https://openalex.org/W2099471712, https://openalex.org/W1686810756, https://openalex.org/W4297775537 |
| referenced_works_count | 43 |
| abstract_inverted_index.a | 20, 26, 52, 103, 119, 180, 188, 209 |
| abstract_inverted_index.In | 30, 185 |
| abstract_inverted_index.To | 90 |
| abstract_inverted_index.We | 116, 207 |
| abstract_inverted_index.an | 35 |
| abstract_inverted_index.at | 15, 187 |
| abstract_inverted_index.by | 86, 126 |
| abstract_inverted_index.in | 197 |
| abstract_inverted_index.is | 6, 23, 68 |
| abstract_inverted_index.of | 11, 55, 71, 105, 122, 130, 140, 143, 183, 233 |
| abstract_inverted_index.to | 203, 214, 231 |
| abstract_inverted_index.we | 33, 101 |
| abstract_inverted_index.The | 48 |
| abstract_inverted_index.and | 9, 58, 81, 95, 109, 113, 132, 157, 166, 220 |
| abstract_inverted_index.are | 161, 200 |
| abstract_inverted_index.for | 25, 40, 61 |
| abstract_inverted_index.has | 137, 228 |
| abstract_inverted_index.low | 181 |
| abstract_inverted_index.map | 111 |
| abstract_inverted_index.our | 75, 151 |
| abstract_inverted_index.the | 56, 92, 106, 123, 128, 134, 141, 144, 158, 171, 192, 198, 221, 225, 234 |
| abstract_inverted_index.use | 102 |
| abstract_inverted_index.JPEG | 42 |
| abstract_inverted_index.also | 117 |
| abstract_inverted_index.core | 53 |
| abstract_inverted_index.deep | 37 |
| abstract_inverted_index.each | 83 |
| abstract_inverted_index.high | 16, 45, 189, 204 |
| abstract_inverted_index.lost | 201 |
| abstract_inverted_index.that | 150, 224 |
| abstract_inverted_index.this | 31 |
| abstract_inverted_index.with | 44, 97, 163 |
| abstract_inverted_index.Joint | 0 |
| abstract_inverted_index.about | 138 |
| abstract_inverted_index.apply | 208 |
| abstract_inverted_index.clear | 162 |
| abstract_inverted_index.down- | 112 |
| abstract_inverted_index.edges | 165 |
| abstract_inverted_index.group | 3 |
| abstract_inverted_index.image | 12, 226 |
| abstract_inverted_index.large | 98 |
| abstract_inverted_index.lossy | 7 |
| abstract_inverted_index.model | 125, 173 |
| abstract_inverted_index.owing | 202 |
| abstract_inverted_index.sharp | 164 |
| abstract_inverted_index.shows | 174 |
| abstract_inverted_index.tasks | 64 |
| abstract_inverted_index.these | 216 |
| abstract_inverted_index.those | 232 |
| abstract_inverted_index.using | 87 |
| abstract_inverted_index.value | 85 |
| abstract_inverted_index.where | 65 |
| abstract_inverted_index.which | 136 |
| abstract_inverted_index.while | 178 |
| abstract_inverted_index.(JPEG) | 4 |
| abstract_inverted_index.global | 73 |
| abstract_inverted_index.ground | 235 |
| abstract_inverted_index.higher | 175 |
| abstract_inverted_index.highly | 217 |
| abstract_inverted_index.image. | 29 |
| abstract_inverted_index.images | 43, 160 |
| abstract_inverted_index.model. | 146 |
| abstract_inverted_index.number | 129, 182 |
| abstract_inverted_index.paper, | 32 |
| abstract_inverted_index.ratio, | 191 |
| abstract_inverted_index.reduce | 91 |
| abstract_inverted_index.reveal | 149, 223 |
| abstract_inverted_index.smooth | 167 |
| abstract_inverted_index.truth. | 236 |
| abstract_inverted_index.vision | 63 |
| abstract_inverted_index.Instead | 70 |
| abstract_inverted_index.blocks, | 199, 219 |
| abstract_inverted_index.changes | 51 |
| abstract_inverted_index.compact | 120, 172 |
| abstract_inverted_index.damaged | 218 |
| abstract_inverted_index.details | 196, 229 |
| abstract_inverted_index.errors. | 206 |
| abstract_inverted_index.experts | 2 |
| abstract_inverted_index.feature | 84, 110 |
| abstract_inverted_index.fields, | 100 |
| abstract_inverted_index.filters | 131 |
| abstract_inverted_index.locally | 78 |
| abstract_inverted_index.network | 60, 76, 152, 212 |
| abstract_inverted_index.overall | 193 |
| abstract_inverted_index.process | 22 |
| abstract_inverted_index.propose | 34, 118 |
| abstract_inverted_index.quality | 13 |
| abstract_inverted_index.ratios. | 18, 47 |
| abstract_inverted_index.restore | 215 |
| abstract_inverted_index.results | 148, 177, 222 |
| abstract_inverted_index.similar | 230 |
| abstract_inverted_index.squeeze | 57 |
| abstract_inverted_index.version | 121 |
| abstract_inverted_index.worsens | 14 |
| abstract_inverted_index.accuracy | 67 |
| abstract_inverted_index.baseline | 145 |
| abstract_inverted_index.blocking | 168 |
| abstract_inverted_index.embedded | 79 |
| abstract_inverted_index.extracts | 77 |
| abstract_inverted_index.features | 80 |
| abstract_inverted_index.learning | 38 |
| abstract_inverted_index.network, | 135 |
| abstract_inverted_index.networks | 155 |
| abstract_inverted_index.pleasant | 28 |
| abstract_inverted_index.produced | 227 |
| abstract_inverted_index.proposed | 49, 124 |
| abstract_inverted_index.required | 24 |
| abstract_inverted_index.restored | 159 |
| abstract_inverted_index.visually | 27 |
| abstract_inverted_index.addition, | 186 |
| abstract_inverted_index.depthwise | 88 |
| abstract_inverted_index.features, | 74 |
| abstract_inverted_index.including | 195 |
| abstract_inverted_index.low-level | 62 |
| abstract_inverted_index.objective | 176 |
| abstract_inverted_index.principle | 54 |
| abstract_inverted_index.receptive | 99 |
| abstract_inverted_index.recursive | 107 |
| abstract_inverted_index.restoring | 41 |
| abstract_inverted_index.structure | 108, 213 |
| abstract_inverted_index.Therefore, | 19 |
| abstract_inverted_index.complexity | 94 |
| abstract_inverted_index.decreasing | 127 |
| abstract_inverted_index.end-to-end | 36 |
| abstract_inverted_index.excitation | 59 |
| abstract_inverted_index.extracting | 72 |
| abstract_inverted_index.fine-tunes | 82 |
| abstract_inverted_index.generative | 210 |
| abstract_inverted_index.important. | 69 |
| abstract_inverted_index.parameters | 96, 142 |
| abstract_inverted_index.processes. | 115 |
| abstract_inverted_index.up-scaling | 114 |
| abstract_inverted_index.adversarial | 211 |
| abstract_inverted_index.boundaries. | 169 |
| abstract_inverted_index.combination | 104 |
| abstract_inverted_index.compression | 5, 17, 46, 190 |
| abstract_inverted_index.degradation | 10 |
| abstract_inverted_index.maintaining | 179 |
| abstract_inverted_index.outperforms | 153 |
| abstract_inverted_index.parameters. | 184 |
| abstract_inverted_index.pixel-level | 66 |
| abstract_inverted_index.simplifying | 133 |
| abstract_inverted_index.Experimental | 147 |
| abstract_inverted_index.Furthermore, | 170 |
| abstract_inverted_index.architecture | 39, 50 |
| abstract_inverted_index.compression, | 8 |
| abstract_inverted_index.conventional | 154 |
| abstract_inverted_index.convolution. | 89 |
| abstract_inverted_index.information, | 194 |
| abstract_inverted_index.photographic | 1 |
| abstract_inverted_index.quantization | 205 |
| abstract_inverted_index.computational | 93 |
| abstract_inverted_index.one-twentieth | 139 |
| abstract_inverted_index.reconstruction | 21 |
| abstract_inverted_index.quantitatively, | 156 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.6600000262260437 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile.value | 0.70196942 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |