Video Coding Based on Ladder Subband Recovery and ResGroup Module Article Swipe
Lichun Wei
,
Aolin Zhang
,
Lei Liu
,
Jun Wang
,
Shuai Wang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/e27070734
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/e27070734
With the rapid development of video encoding technology in the field of computer vision, the demand for tasks such as video frame reconstruction, denoising, and super-resolution has been continuously increasing. However, traditional video encoding methods typically focus on extracting spatial or temporal domain information, often facing challenges of insufficient accuracy and information loss when reconstructing high-frequency details, edges, and textures of images. To address this issue, this paper proposes an innovative LadderConv framework, which combines discrete wavelet transform (DWT) with spatial and channel attention mechanisms. By progressively recovering wavelet subbands, it effectively enhances the video frame encoding quality. Specifically, the LadderConv framework adopts a stepwise recovery approach for wavelet subbands, first processing high-frequency detail subbands with relatively less information, then enhancing the interaction between these subbands, and ultimately synthesizing a high-quality reconstructed image through inverse wavelet transform. Moreover, the framework introduces spatial and channel attention mechanisms, which further strengthen the focus on key regions and channel features, leading to notable improvements in detail restoration and image reconstruction accuracy. To optimize the performance of the LadderConv framework, particularly in detail recovery and high-frequency information extraction tasks, this paper designs an innovative ResGroup module. By using multi-layer convolution operations along with feature map compression and recovery, the ResGroup module enhances the network’s expressive capability and effectively reduces computational complexity. The ResGroup module captures multi-level features from low level to high level and retains rich feature information through residual connections, thus improving the overall reconstruction performance of the model. In experiments, the combination of the LadderConv framework and the ResGroup module demonstrates superior performance in video frame reconstruction tasks, particularly in recovering high-frequency information, image clarity, and detail representation.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/e27070734
- OA Status
- gold
- References
- 23
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4412369259
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4412369259Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/e27070734Digital Object Identifier
- Title
-
Video Coding Based on Ladder Subband Recovery and ResGroup ModuleWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-07-08Full publication date if available
- Authors
-
Lichun Wei, Aolin Zhang, Lei Liu, Jun Wang, Shuai WangList of authors in order
- Landing page
-
https://doi.org/10.3390/e27070734Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.3390/e27070734Direct OA link when available
- Concepts
-
Computer science, Artificial intelligence, Wavelet, Encoding (memory), Computer vision, Discrete wavelet transform, Focus (optics), Feature (linguistics), Wavelet transform, Coding (social sciences), Pattern recognition (psychology), Physics, Mathematics, Statistics, Philosophy, Linguistics, OpticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
23Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4412369259 |
|---|---|
| doi | https://doi.org/10.3390/e27070734 |
| ids.doi | https://doi.org/10.3390/e27070734 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/40724450 |
| ids.openalex | https://openalex.org/W4412369259 |
| fwci | 0.0 |
| type | article |
| title | Video Coding Based on Ladder Subband Recovery and ResGroup Module |
| awards[0].id | https://openalex.org/G8307653903 |
| awards[0].funder_id | https://openalex.org/F4320322163 |
| awards[0].display_name | |
| awards[0].funder_award_id | F2022201013 |
| awards[0].funder_display_name | Natural Science Foundation of Hebei Province |
| biblio.issue | 7 |
| biblio.volume | 27 |
| biblio.last_page | 734 |
| biblio.first_page | 734 |
| 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 | 0.9998999834060669 |
| 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 | 0.9995999932289124 |
| 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/T11019 |
| 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 | Image Enhancement Techniques |
| funders[0].id | https://openalex.org/F4320322163 |
| funders[0].ror | https://ror.org/01h0zpd94 |
| funders[0].display_name | Natural Science Foundation of Hebei Province |
| is_xpac | False |
| apc_list.value | 2000 |
| apc_list.currency | CHF |
| apc_list.value_usd | 2165 |
| apc_paid.value | 2000 |
| apc_paid.currency | CHF |
| apc_paid.value_usd | 2165 |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.8051992058753967 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C154945302 |
| concepts[1].level | 1 |
| concepts[1].score | 0.5871661305427551 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[1].display_name | Artificial intelligence |
| concepts[2].id | https://openalex.org/C47432892 |
| concepts[2].level | 2 |
| concepts[2].score | 0.5278223156929016 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q831390 |
| concepts[2].display_name | Wavelet |
| concepts[3].id | https://openalex.org/C125411270 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5117242336273193 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q18653 |
| concepts[3].display_name | Encoding (memory) |
| concepts[4].id | https://openalex.org/C31972630 |
| concepts[4].level | 1 |
| concepts[4].score | 0.5023493766784668 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q844240 |
| concepts[4].display_name | Computer vision |
| concepts[5].id | https://openalex.org/C46286280 |
| concepts[5].level | 4 |
| concepts[5].score | 0.49631959199905396 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q2414958 |
| concepts[5].display_name | Discrete wavelet transform |
| concepts[6].id | https://openalex.org/C192209626 |
| concepts[6].level | 2 |
| concepts[6].score | 0.48716995120048523 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q190909 |
| concepts[6].display_name | Focus (optics) |
| concepts[7].id | https://openalex.org/C2776401178 |
| concepts[7].level | 2 |
| concepts[7].score | 0.436886191368103 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q12050496 |
| concepts[7].display_name | Feature (linguistics) |
| concepts[8].id | https://openalex.org/C196216189 |
| concepts[8].level | 3 |
| concepts[8].score | 0.4219839572906494 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q2867 |
| concepts[8].display_name | Wavelet transform |
| concepts[9].id | https://openalex.org/C179518139 |
| concepts[9].level | 2 |
| concepts[9].score | 0.41965189576148987 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q5140297 |
| concepts[9].display_name | Coding (social sciences) |
| concepts[10].id | https://openalex.org/C153180895 |
| concepts[10].level | 2 |
| concepts[10].score | 0.3330181837081909 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[10].display_name | Pattern recognition (psychology) |
| concepts[11].id | https://openalex.org/C121332964 |
| concepts[11].level | 0 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[11].display_name | Physics |
| concepts[12].id | https://openalex.org/C33923547 |
| concepts[12].level | 0 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[12].display_name | Mathematics |
| concepts[13].id | https://openalex.org/C105795698 |
| concepts[13].level | 1 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q12483 |
| concepts[13].display_name | Statistics |
| concepts[14].id | https://openalex.org/C138885662 |
| concepts[14].level | 0 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q5891 |
| concepts[14].display_name | Philosophy |
| concepts[15].id | https://openalex.org/C41895202 |
| concepts[15].level | 1 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q8162 |
| concepts[15].display_name | Linguistics |
| concepts[16].id | https://openalex.org/C120665830 |
| concepts[16].level | 1 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q14620 |
| concepts[16].display_name | Optics |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.8051992058753967 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[1].score | 0.5871661305427551 |
| keywords[1].display_name | Artificial intelligence |
| keywords[2].id | https://openalex.org/keywords/wavelet |
| keywords[2].score | 0.5278223156929016 |
| keywords[2].display_name | Wavelet |
| keywords[3].id | https://openalex.org/keywords/encoding |
| keywords[3].score | 0.5117242336273193 |
| keywords[3].display_name | Encoding (memory) |
| keywords[4].id | https://openalex.org/keywords/computer-vision |
| keywords[4].score | 0.5023493766784668 |
| keywords[4].display_name | Computer vision |
| keywords[5].id | https://openalex.org/keywords/discrete-wavelet-transform |
| keywords[5].score | 0.49631959199905396 |
| keywords[5].display_name | Discrete wavelet transform |
| keywords[6].id | https://openalex.org/keywords/focus |
| keywords[6].score | 0.48716995120048523 |
| keywords[6].display_name | Focus (optics) |
| keywords[7].id | https://openalex.org/keywords/feature |
| keywords[7].score | 0.436886191368103 |
| keywords[7].display_name | Feature (linguistics) |
| keywords[8].id | https://openalex.org/keywords/wavelet-transform |
| keywords[8].score | 0.4219839572906494 |
| keywords[8].display_name | Wavelet transform |
| keywords[9].id | https://openalex.org/keywords/coding |
| keywords[9].score | 0.41965189576148987 |
| keywords[9].display_name | Coding (social sciences) |
| keywords[10].id | https://openalex.org/keywords/pattern-recognition |
| keywords[10].score | 0.3330181837081909 |
| keywords[10].display_name | Pattern recognition (psychology) |
| language | en |
| locations[0].id | doi:10.3390/e27070734 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S195231649 |
| locations[0].source.issn | 1099-4300 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 1099-4300 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Entropy |
| locations[0].source.host_organization | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| locations[0].license | cc-by |
| locations[0].pdf_url | |
| 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 | Entropy |
| locations[0].landing_page_url | https://doi.org/10.3390/e27070734 |
| locations[1].id | pmid:40724450 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306525036 |
| 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 | PubMed |
| locations[1].source.host_organization | https://openalex.org/I1299303238 |
| locations[1].source.host_organization_name | National Institutes of Health |
| locations[1].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | publishedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | True |
| locations[1].raw_source_name | Entropy (Basel, Switzerland) |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/40724450 |
| locations[2].id | pmh:oai:doaj.org/article:bb9a5d5cf41a46a7bf504930a4b11551 |
| locations[2].is_oa | False |
| locations[2].source.id | https://openalex.org/S4306401280 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | False |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[2].source.host_organization | |
| locations[2].source.host_organization_name | |
| locations[2].license | |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | article |
| locations[2].license_id | |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | Entropy, Vol 27, Iss 7, p 734 (2025) |
| locations[2].landing_page_url | https://doaj.org/article/bb9a5d5cf41a46a7bf504930a4b11551 |
| locations[3].id | pmh:oai:pubmedcentral.nih.gov:12296159 |
| locations[3].is_oa | True |
| locations[3].source.id | https://openalex.org/S2764455111 |
| locations[3].source.issn | |
| locations[3].source.type | repository |
| locations[3].source.is_oa | False |
| locations[3].source.issn_l | |
| locations[3].source.is_core | False |
| locations[3].source.is_in_doaj | False |
| locations[3].source.display_name | PubMed Central |
| locations[3].source.host_organization | https://openalex.org/I1299303238 |
| locations[3].source.host_organization_name | National Institutes of Health |
| locations[3].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[3].license | other-oa |
| locations[3].pdf_url | |
| locations[3].version | submittedVersion |
| locations[3].raw_type | Text |
| locations[3].license_id | https://openalex.org/licenses/other-oa |
| locations[3].is_accepted | False |
| locations[3].is_published | False |
| locations[3].raw_source_name | Entropy (Basel) |
| locations[3].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/12296159 |
| indexed_in | crossref, doaj, pubmed |
| authorships[0].author.id | https://openalex.org/A5103272528 |
| authorships[0].author.orcid | https://orcid.org/0009-0008-4878-3645 |
| authorships[0].author.display_name | Lichun Wei |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I43337087 |
| authorships[0].affiliations[0].raw_affiliation_string | School of Mathematics and Information Science, Hebei University, Baoding 071000, China |
| authorships[0].institutions[0].id | https://openalex.org/I43337087 |
| authorships[0].institutions[0].ror | https://ror.org/01p884a79 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I43337087 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | Hebei University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Libo Wei |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | School of Mathematics and Information Science, Hebei University, Baoding 071000, China |
| authorships[1].author.id | https://openalex.org/A5089304765 |
| authorships[1].author.orcid | https://orcid.org/0009-0009-7875-4009 |
| authorships[1].author.display_name | Aolin Zhang |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I43337087 |
| authorships[1].affiliations[0].raw_affiliation_string | School of Mathematics and Information Science, Hebei University, Baoding 071000, China |
| authorships[1].institutions[0].id | https://openalex.org/I43337087 |
| authorships[1].institutions[0].ror | https://ror.org/01p884a79 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I43337087 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | Hebei University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Aolin Zhang |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | School of Mathematics and Information Science, Hebei University, Baoding 071000, China |
| authorships[2].author.id | https://openalex.org/A5087865255 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-1962-8856 |
| authorships[2].author.display_name | Lei Liu |
| authorships[2].countries | CN |
| authorships[2].affiliations[0].raw_affiliation_string | Huaibei Key Laboratory of Digital Multimedia Intelligent Information Processing, Huaibei 235000, China |
| authorships[2].affiliations[1].institution_ids | https://openalex.org/I165859042 |
| authorships[2].affiliations[1].raw_affiliation_string | School of Computer Science and Technology, Huaibei Normal University, Huaibei 235000, China |
| authorships[2].institutions[0].id | https://openalex.org/I165859042 |
| authorships[2].institutions[0].ror | https://ror.org/03ek23472 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I165859042 |
| authorships[2].institutions[0].country_code | CN |
| authorships[2].institutions[0].display_name | Huaibei Normal University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Lei Liu |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Huaibei Key Laboratory of Digital Multimedia Intelligent Information Processing, Huaibei 235000, China, School of Computer Science and Technology, Huaibei Normal University, Huaibei 235000, China |
| authorships[3].author.id | https://openalex.org/A5100335043 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-5901-9019 |
| authorships[3].author.display_name | Jun Wang |
| authorships[3].countries | CN |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I43337087 |
| authorships[3].affiliations[0].raw_affiliation_string | College of Electronic and Information Engineering, Hebei University, Baoding 071000, China |
| authorships[3].institutions[0].id | https://openalex.org/I43337087 |
| authorships[3].institutions[0].ror | https://ror.org/01p884a79 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I43337087 |
| authorships[3].institutions[0].country_code | CN |
| authorships[3].institutions[0].display_name | Hebei University |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Jun Wang |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | College of Electronic and Information Engineering, Hebei University, Baoding 071000, China |
| authorships[4].author.id | https://openalex.org/A5013202783 |
| authorships[4].author.orcid | https://orcid.org/0009-0004-2206-1047 |
| authorships[4].author.display_name | Shuai Wang |
| authorships[4].countries | CN |
| authorships[4].affiliations[0].raw_affiliation_string | Huaibei Key Laboratory of Digital Multimedia Intelligent Information Processing, Huaibei 235000, China |
| authorships[4].affiliations[1].institution_ids | https://openalex.org/I165859042 |
| authorships[4].affiliations[1].raw_affiliation_string | School of Computer Science and Technology, Huaibei Normal University, Huaibei 235000, China |
| authorships[4].institutions[0].id | https://openalex.org/I165859042 |
| authorships[4].institutions[0].ror | https://ror.org/03ek23472 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I165859042 |
| authorships[4].institutions[0].country_code | CN |
| authorships[4].institutions[0].display_name | Huaibei Normal University |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Shuai Wang |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Huaibei Key Laboratory of Digital Multimedia Intelligent Information Processing, Huaibei 235000, China, School of Computer Science and Technology, Huaibei Normal University, Huaibei 235000, China |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.3390/e27070734 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Video Coding Based on Ladder Subband Recovery and ResGroup Module |
| 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 | 0.9998999834060669 |
| 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/W183670115, https://openalex.org/W4245420407, https://openalex.org/W1501179639, https://openalex.org/W3199035354, https://openalex.org/W2085792030, https://openalex.org/W4401371153, https://openalex.org/W1807354010, https://openalex.org/W3143644526, https://openalex.org/W598225674, https://openalex.org/W1588899229 |
| cited_by_count | 0 |
| locations_count | 4 |
| best_oa_location.id | doi:10.3390/e27070734 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S195231649 |
| best_oa_location.source.issn | 1099-4300 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 1099-4300 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Entropy |
| best_oa_location.source.host_organization | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | |
| 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 | Entropy |
| best_oa_location.landing_page_url | https://doi.org/10.3390/e27070734 |
| primary_location.id | doi:10.3390/e27070734 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S195231649 |
| primary_location.source.issn | 1099-4300 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 1099-4300 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Entropy |
| primary_location.source.host_organization | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| primary_location.license | cc-by |
| primary_location.pdf_url | |
| 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 | Entropy |
| primary_location.landing_page_url | https://doi.org/10.3390/e27070734 |
| publication_date | 2025-07-08 |
| publication_year | 2025 |
| referenced_works | https://openalex.org/W2146395539, https://openalex.org/W2891639355, https://openalex.org/W3202918664, https://openalex.org/W3003173865, https://openalex.org/W3160366495, https://openalex.org/W3111674505, https://openalex.org/W3020741905, https://openalex.org/W2973673960, https://openalex.org/W4308236046, https://openalex.org/W3028468376, https://openalex.org/W4388579690, https://openalex.org/W4390872862, https://openalex.org/W3035006120, https://openalex.org/W3043823510, https://openalex.org/W3166873596, https://openalex.org/W3173342677, https://openalex.org/W4312254122, https://openalex.org/W3034469748, https://openalex.org/W4362496227, https://openalex.org/W3175457126, https://openalex.org/W4221147755, https://openalex.org/W4223425316, https://openalex.org/W3206996732 |
| referenced_works_count | 23 |
| abstract_inverted_index.a | 103, 129 |
| abstract_inverted_index.By | 85, 192 |
| abstract_inverted_index.In | 246 |
| abstract_inverted_index.To | 62, 168 |
| abstract_inverted_index.an | 69, 188 |
| abstract_inverted_index.as | 19 |
| abstract_inverted_index.in | 8, 161, 177, 261, 267 |
| abstract_inverted_index.it | 90 |
| abstract_inverted_index.of | 4, 11, 47, 60, 172, 243, 250 |
| abstract_inverted_index.on | 37, 151 |
| abstract_inverted_index.or | 40 |
| abstract_inverted_index.to | 158, 226 |
| abstract_inverted_index.The | 217 |
| abstract_inverted_index.and | 24, 50, 58, 81, 126, 142, 154, 164, 180, 202, 212, 229, 254, 273 |
| abstract_inverted_index.for | 16, 107 |
| abstract_inverted_index.has | 26 |
| abstract_inverted_index.key | 152 |
| abstract_inverted_index.low | 224 |
| abstract_inverted_index.map | 200 |
| abstract_inverted_index.the | 1, 9, 14, 93, 99, 121, 138, 149, 170, 173, 204, 208, 239, 244, 248, 251, 255 |
| abstract_inverted_index.With | 0 |
| abstract_inverted_index.been | 27 |
| abstract_inverted_index.from | 223 |
| abstract_inverted_index.high | 227 |
| abstract_inverted_index.less | 117 |
| abstract_inverted_index.loss | 52 |
| abstract_inverted_index.rich | 231 |
| abstract_inverted_index.such | 18 |
| abstract_inverted_index.then | 119 |
| abstract_inverted_index.this | 64, 66, 185 |
| abstract_inverted_index.thus | 237 |
| abstract_inverted_index.when | 53 |
| abstract_inverted_index.with | 79, 115, 198 |
| abstract_inverted_index.(DWT) | 78 |
| abstract_inverted_index.along | 197 |
| abstract_inverted_index.field | 10 |
| abstract_inverted_index.first | 110 |
| abstract_inverted_index.focus | 36, 150 |
| abstract_inverted_index.frame | 21, 95, 263 |
| abstract_inverted_index.image | 132, 165, 271 |
| abstract_inverted_index.level | 225, 228 |
| abstract_inverted_index.often | 44 |
| abstract_inverted_index.paper | 67, 186 |
| abstract_inverted_index.rapid | 2 |
| abstract_inverted_index.tasks | 17 |
| abstract_inverted_index.these | 124 |
| abstract_inverted_index.using | 193 |
| abstract_inverted_index.video | 5, 20, 32, 94, 262 |
| abstract_inverted_index.which | 73, 146 |
| abstract_inverted_index.adopts | 102 |
| abstract_inverted_index.demand | 15 |
| abstract_inverted_index.detail | 113, 162, 178, 274 |
| abstract_inverted_index.domain | 42 |
| abstract_inverted_index.edges, | 57 |
| abstract_inverted_index.facing | 45 |
| abstract_inverted_index.issue, | 65 |
| abstract_inverted_index.model. | 245 |
| abstract_inverted_index.module | 206, 219, 257 |
| abstract_inverted_index.tasks, | 184, 265 |
| abstract_inverted_index.address | 63 |
| abstract_inverted_index.between | 123 |
| abstract_inverted_index.channel | 82, 143, 155 |
| abstract_inverted_index.designs | 187 |
| abstract_inverted_index.feature | 199, 232 |
| abstract_inverted_index.further | 147 |
| abstract_inverted_index.images. | 61 |
| abstract_inverted_index.inverse | 134 |
| abstract_inverted_index.leading | 157 |
| abstract_inverted_index.methods | 34 |
| abstract_inverted_index.module. | 191 |
| abstract_inverted_index.notable | 159 |
| abstract_inverted_index.overall | 240 |
| abstract_inverted_index.reduces | 214 |
| abstract_inverted_index.regions | 153 |
| abstract_inverted_index.retains | 230 |
| abstract_inverted_index.spatial | 39, 80, 141 |
| abstract_inverted_index.through | 133, 234 |
| abstract_inverted_index.vision, | 13 |
| abstract_inverted_index.wavelet | 76, 88, 108, 135 |
| abstract_inverted_index.However, | 30 |
| abstract_inverted_index.ResGroup | 190, 205, 218, 256 |
| abstract_inverted_index.accuracy | 49 |
| abstract_inverted_index.approach | 106 |
| abstract_inverted_index.captures | 220 |
| abstract_inverted_index.clarity, | 272 |
| abstract_inverted_index.combines | 74 |
| abstract_inverted_index.computer | 12 |
| abstract_inverted_index.details, | 56 |
| abstract_inverted_index.discrete | 75 |
| abstract_inverted_index.encoding | 6, 33, 96 |
| abstract_inverted_index.enhances | 92, 207 |
| abstract_inverted_index.features | 222 |
| abstract_inverted_index.optimize | 169 |
| abstract_inverted_index.proposes | 68 |
| abstract_inverted_index.quality. | 97 |
| abstract_inverted_index.recovery | 105, 179 |
| abstract_inverted_index.residual | 235 |
| abstract_inverted_index.stepwise | 104 |
| abstract_inverted_index.subbands | 114 |
| abstract_inverted_index.superior | 259 |
| abstract_inverted_index.temporal | 41 |
| abstract_inverted_index.textures | 59 |
| abstract_inverted_index.Moreover, | 137 |
| abstract_inverted_index.accuracy. | 167 |
| abstract_inverted_index.attention | 83, 144 |
| abstract_inverted_index.enhancing | 120 |
| abstract_inverted_index.features, | 156 |
| abstract_inverted_index.framework | 101, 139, 253 |
| abstract_inverted_index.improving | 238 |
| abstract_inverted_index.recovery, | 203 |
| abstract_inverted_index.subbands, | 89, 109, 125 |
| abstract_inverted_index.transform | 77 |
| abstract_inverted_index.typically | 35 |
| abstract_inverted_index.LadderConv | 71, 100, 174, 252 |
| abstract_inverted_index.capability | 211 |
| abstract_inverted_index.challenges | 46 |
| abstract_inverted_index.denoising, | 23 |
| abstract_inverted_index.expressive | 210 |
| abstract_inverted_index.extracting | 38 |
| abstract_inverted_index.extraction | 183 |
| abstract_inverted_index.framework, | 72, 175 |
| abstract_inverted_index.innovative | 70, 189 |
| abstract_inverted_index.introduces | 140 |
| abstract_inverted_index.operations | 196 |
| abstract_inverted_index.processing | 111 |
| abstract_inverted_index.recovering | 87, 268 |
| abstract_inverted_index.relatively | 116 |
| abstract_inverted_index.strengthen | 148 |
| abstract_inverted_index.technology | 7 |
| abstract_inverted_index.transform. | 136 |
| abstract_inverted_index.ultimately | 127 |
| abstract_inverted_index.combination | 249 |
| abstract_inverted_index.complexity. | 216 |
| abstract_inverted_index.compression | 201 |
| abstract_inverted_index.convolution | 195 |
| abstract_inverted_index.development | 3 |
| abstract_inverted_index.effectively | 91, 213 |
| abstract_inverted_index.increasing. | 29 |
| abstract_inverted_index.information | 51, 182, 233 |
| abstract_inverted_index.interaction | 122 |
| abstract_inverted_index.mechanisms, | 145 |
| abstract_inverted_index.mechanisms. | 84 |
| abstract_inverted_index.multi-layer | 194 |
| abstract_inverted_index.multi-level | 221 |
| abstract_inverted_index.network’s | 209 |
| abstract_inverted_index.performance | 171, 242, 260 |
| abstract_inverted_index.restoration | 163 |
| abstract_inverted_index.traditional | 31 |
| abstract_inverted_index.connections, | 236 |
| abstract_inverted_index.continuously | 28 |
| abstract_inverted_index.demonstrates | 258 |
| abstract_inverted_index.experiments, | 247 |
| abstract_inverted_index.high-quality | 130 |
| abstract_inverted_index.improvements | 160 |
| abstract_inverted_index.information, | 43, 118, 270 |
| abstract_inverted_index.insufficient | 48 |
| abstract_inverted_index.particularly | 176, 266 |
| abstract_inverted_index.synthesizing | 128 |
| abstract_inverted_index.Specifically, | 98 |
| abstract_inverted_index.computational | 215 |
| abstract_inverted_index.progressively | 86 |
| abstract_inverted_index.reconstructed | 131 |
| abstract_inverted_index.high-frequency | 55, 112, 181, 269 |
| abstract_inverted_index.reconstructing | 54 |
| abstract_inverted_index.reconstruction | 166, 241, 264 |
| abstract_inverted_index.reconstruction, | 22 |
| abstract_inverted_index.representation. | 275 |
| abstract_inverted_index.super-resolution | 25 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.6000000238418579 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile.value | 0.27518837 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |