ClearNet: auto-encoder based denoising model for endoscopy images Article Swipe
Vikrant Shokeen
,
Sandeep Kumar
,
Vidhu Mathur
,
Sharma Amit
,
Indrajeet Gupta
,
Parita Jain
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.11591/ijeecs.v38.i3.pp1990-2000
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.11591/ijeecs.v38.i3.pp1990-2000
Gastrointestinal (GI) endoscopy images play a crucial role in the detection and diagnosis of diseases within the digestive tract. However, the development of effective computer vision models for automated analysis and denoising of endoscopy images faces challenges arising from the diverse nature of abnormalities and the presence of image artefacts. In this work, the utilization of an encoder-decoder network for denoising GI endoscopy images using the HyperKvasir dataset has been analyzed. This approach involves training a custom encoder-decoder model on this extensive multiclass endoscopy image dataset and assessing its performance across 23 prevalent classes of digestive tract issues. Here experiments showcase the model’s ability to learn robust visual representations from endoscopic data, enabling accurate disease prediction. The achieved promising results highlight the potential of encoder-decoder architectures as a foundational framework for computer-aided endoscopy analysis with a specific focus on denoising applications. Our model manages to increase the peak signal-tonoise ratio (PSNR) of original-noisy pair from 19.118954 to 69.892631 for original-reconstructed pair showcasing almost perfect reconstruction.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.11591/ijeecs.v38.i3.pp1990-2000
- OA Status
- diamond
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4410157608
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4410157608Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.11591/ijeecs.v38.i3.pp1990-2000Digital Object Identifier
- Title
-
ClearNet: auto-encoder based denoising model for endoscopy imagesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-05-07Full publication date if available
- Authors
-
Vikrant Shokeen, Sandeep Kumar, Vidhu Mathur, Sharma Amit, Indrajeet Gupta, Parita JainList of authors in order
- Landing page
-
https://doi.org/10.11591/ijeecs.v38.i3.pp1990-2000Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.11591/ijeecs.v38.i3.pp1990-2000Direct OA link when available
- Concepts
-
Noise reduction, Encoder, Computer vision, Artificial intelligence, Computer science, Image denoising, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4410157608 |
|---|---|
| doi | https://doi.org/10.11591/ijeecs.v38.i3.pp1990-2000 |
| ids.doi | https://doi.org/10.11591/ijeecs.v38.i3.pp1990-2000 |
| ids.openalex | https://openalex.org/W4410157608 |
| fwci | 0.0 |
| type | article |
| title | ClearNet: auto-encoder based denoising model for endoscopy images |
| biblio.issue | 3 |
| biblio.volume | 38 |
| biblio.last_page | 1990 |
| biblio.first_page | 1990 |
| topics[0].id | https://openalex.org/T10901 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.7452999949455261 |
| 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 Data Compression Techniques |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C163294075 |
| concepts[0].level | 2 |
| concepts[0].score | 0.6287578344345093 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q581861 |
| concepts[0].display_name | Noise reduction |
| concepts[1].id | https://openalex.org/C118505674 |
| concepts[1].level | 2 |
| concepts[1].score | 0.5906355381011963 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q42586063 |
| concepts[1].display_name | Encoder |
| concepts[2].id | https://openalex.org/C31972630 |
| concepts[2].level | 1 |
| concepts[2].score | 0.5621652007102966 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q844240 |
| concepts[2].display_name | Computer vision |
| concepts[3].id | https://openalex.org/C154945302 |
| concepts[3].level | 1 |
| concepts[3].score | 0.5417324900627136 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[3].display_name | Artificial intelligence |
| concepts[4].id | https://openalex.org/C41008148 |
| concepts[4].level | 0 |
| concepts[4].score | 0.541146457195282 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[4].display_name | Computer science |
| concepts[5].id | https://openalex.org/C2983327147 |
| concepts[5].level | 3 |
| concepts[5].score | 0.505029022693634 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q581861 |
| concepts[5].display_name | Image denoising |
| concepts[6].id | https://openalex.org/C111919701 |
| concepts[6].level | 1 |
| concepts[6].score | 0.0 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q9135 |
| concepts[6].display_name | Operating system |
| keywords[0].id | https://openalex.org/keywords/noise-reduction |
| keywords[0].score | 0.6287578344345093 |
| keywords[0].display_name | Noise reduction |
| keywords[1].id | https://openalex.org/keywords/encoder |
| keywords[1].score | 0.5906355381011963 |
| keywords[1].display_name | Encoder |
| keywords[2].id | https://openalex.org/keywords/computer-vision |
| keywords[2].score | 0.5621652007102966 |
| keywords[2].display_name | Computer vision |
| keywords[3].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[3].score | 0.5417324900627136 |
| keywords[3].display_name | Artificial intelligence |
| keywords[4].id | https://openalex.org/keywords/computer-science |
| keywords[4].score | 0.541146457195282 |
| keywords[4].display_name | Computer science |
| keywords[5].id | https://openalex.org/keywords/image-denoising |
| keywords[5].score | 0.505029022693634 |
| keywords[5].display_name | Image denoising |
| language | en |
| locations[0].id | doi:10.11591/ijeecs.v38.i3.pp1990-2000 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S2764855249 |
| locations[0].source.issn | 2502-4752, 2502-4760 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2502-4752 |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Indonesian Journal of Electrical Engineering and Computer Science |
| locations[0].source.host_organization | https://openalex.org/P4310315009 |
| locations[0].source.host_organization_name | Institute of Advanced Engineering and Science (IAES) |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310315009 |
| locations[0].source.host_organization_lineage_names | Institute of Advanced Engineering and Science (IAES) |
| locations[0].license | |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Indonesian Journal of Electrical Engineering and Computer Science |
| locations[0].landing_page_url | https://doi.org/10.11591/ijeecs.v38.i3.pp1990-2000 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5080116984 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-6366-8580 |
| authorships[0].author.display_name | Vikrant Shokeen |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Vikrant Shokeen |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5035864009 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-4550-4814 |
| authorships[1].author.display_name | Sandeep Kumar |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Sandeep Kumar |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5032930066 |
| authorships[2].author.orcid | |
| authorships[2].author.display_name | Vidhu Mathur |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Vidhu Mathur |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5101179227 |
| authorships[3].author.orcid | https://orcid.org/0000-0001-6190-4861 |
| authorships[3].author.display_name | Sharma Amit |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Amit Sharma |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5012338428 |
| authorships[4].author.orcid | https://orcid.org/0000-0003-1262-899X |
| authorships[4].author.display_name | Indrajeet Gupta |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Indrajeet Gupta |
| authorships[4].is_corresponding | False |
| authorships[5].author.id | https://openalex.org/A5053185855 |
| authorships[5].author.orcid | https://orcid.org/0000-0002-7626-7786 |
| authorships[5].author.display_name | Parita Jain |
| authorships[5].author_position | last |
| authorships[5].raw_author_name | Parita Jain |
| authorships[5].is_corresponding | False |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.11591/ijeecs.v38.i3.pp1990-2000 |
| open_access.oa_status | diamond |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | ClearNet: auto-encoder based denoising model for endoscopy images |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10901 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.7452999949455261 |
| 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 Data Compression Techniques |
| related_works | https://openalex.org/W2772917594, https://openalex.org/W2036807459, https://openalex.org/W2058170566, https://openalex.org/W2755342338, https://openalex.org/W2166024367, https://openalex.org/W3116076068, https://openalex.org/W2229312674, https://openalex.org/W2951359407, https://openalex.org/W2079911747, https://openalex.org/W1969923398 |
| cited_by_count | 0 |
| locations_count | 1 |
| best_oa_location.id | doi:10.11591/ijeecs.v38.i3.pp1990-2000 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S2764855249 |
| best_oa_location.source.issn | 2502-4752, 2502-4760 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2502-4752 |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Indonesian Journal of Electrical Engineering and Computer Science |
| best_oa_location.source.host_organization | https://openalex.org/P4310315009 |
| best_oa_location.source.host_organization_name | Institute of Advanced Engineering and Science (IAES) |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310315009 |
| best_oa_location.source.host_organization_lineage_names | Institute of Advanced Engineering and Science (IAES) |
| best_oa_location.license | |
| best_oa_location.pdf_url | |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Indonesian Journal of Electrical Engineering and Computer Science |
| best_oa_location.landing_page_url | https://doi.org/10.11591/ijeecs.v38.i3.pp1990-2000 |
| primary_location.id | doi:10.11591/ijeecs.v38.i3.pp1990-2000 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S2764855249 |
| primary_location.source.issn | 2502-4752, 2502-4760 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2502-4752 |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Indonesian Journal of Electrical Engineering and Computer Science |
| primary_location.source.host_organization | https://openalex.org/P4310315009 |
| primary_location.source.host_organization_name | Institute of Advanced Engineering and Science (IAES) |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310315009 |
| primary_location.source.host_organization_lineage_names | Institute of Advanced Engineering and Science (IAES) |
| primary_location.license | |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Indonesian Journal of Electrical Engineering and Computer Science |
| primary_location.landing_page_url | https://doi.org/10.11591/ijeecs.v38.i3.pp1990-2000 |
| publication_date | 2025-05-07 |
| publication_year | 2025 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 5, 75, 127, 135 |
| abstract_inverted_index.23 | 91 |
| abstract_inverted_index.GI | 61 |
| abstract_inverted_index.In | 50 |
| abstract_inverted_index.an | 56 |
| abstract_inverted_index.as | 126 |
| abstract_inverted_index.in | 8 |
| abstract_inverted_index.of | 13, 22, 32, 42, 47, 55, 94, 123, 151 |
| abstract_inverted_index.on | 79, 138 |
| abstract_inverted_index.to | 104, 144, 156 |
| abstract_inverted_index.Our | 141 |
| abstract_inverted_index.The | 116 |
| abstract_inverted_index.and | 11, 30, 44, 86 |
| abstract_inverted_index.for | 27, 59, 130, 158 |
| abstract_inverted_index.has | 68 |
| abstract_inverted_index.its | 88 |
| abstract_inverted_index.the | 9, 16, 20, 39, 45, 53, 65, 101, 121, 146 |
| abstract_inverted_index.(GI) | 1 |
| abstract_inverted_index.Here | 98 |
| abstract_inverted_index.This | 71 |
| abstract_inverted_index.been | 69 |
| abstract_inverted_index.from | 38, 109, 154 |
| abstract_inverted_index.pair | 153, 160 |
| abstract_inverted_index.peak | 147 |
| abstract_inverted_index.play | 4 |
| abstract_inverted_index.role | 7 |
| abstract_inverted_index.this | 51, 80 |
| abstract_inverted_index.with | 134 |
| abstract_inverted_index.data, | 111 |
| abstract_inverted_index.faces | 35 |
| abstract_inverted_index.focus | 137 |
| abstract_inverted_index.image | 48, 84 |
| abstract_inverted_index.learn | 105 |
| abstract_inverted_index.model | 78, 142 |
| abstract_inverted_index.ratio | 149 |
| abstract_inverted_index.tract | 96 |
| abstract_inverted_index.using | 64 |
| abstract_inverted_index.work, | 52 |
| abstract_inverted_index.(PSNR) | 150 |
| abstract_inverted_index.across | 90 |
| abstract_inverted_index.almost | 162 |
| abstract_inverted_index.custom | 76 |
| abstract_inverted_index.images | 3, 34, 63 |
| abstract_inverted_index.models | 26 |
| abstract_inverted_index.nature | 41 |
| abstract_inverted_index.robust | 106 |
| abstract_inverted_index.tract. | 18 |
| abstract_inverted_index.vision | 25 |
| abstract_inverted_index.visual | 107 |
| abstract_inverted_index.within | 15 |
| abstract_inverted_index.ability | 103 |
| abstract_inverted_index.arising | 37 |
| abstract_inverted_index.classes | 93 |
| abstract_inverted_index.crucial | 6 |
| abstract_inverted_index.dataset | 67, 85 |
| abstract_inverted_index.disease | 114 |
| abstract_inverted_index.diverse | 40 |
| abstract_inverted_index.issues. | 97 |
| abstract_inverted_index.manages | 143 |
| abstract_inverted_index.network | 58 |
| abstract_inverted_index.perfect | 163 |
| abstract_inverted_index.results | 119 |
| abstract_inverted_index.However, | 19 |
| abstract_inverted_index.accurate | 113 |
| abstract_inverted_index.achieved | 117 |
| abstract_inverted_index.analysis | 29, 133 |
| abstract_inverted_index.approach | 72 |
| abstract_inverted_index.computer | 24 |
| abstract_inverted_index.diseases | 14 |
| abstract_inverted_index.enabling | 112 |
| abstract_inverted_index.increase | 145 |
| abstract_inverted_index.involves | 73 |
| abstract_inverted_index.presence | 46 |
| abstract_inverted_index.showcase | 100 |
| abstract_inverted_index.specific | 136 |
| abstract_inverted_index.training | 74 |
| abstract_inverted_index.19.118954 | 155 |
| abstract_inverted_index.69.892631 | 157 |
| abstract_inverted_index.analyzed. | 70 |
| abstract_inverted_index.assessing | 87 |
| abstract_inverted_index.automated | 28 |
| abstract_inverted_index.denoising | 31, 60, 139 |
| abstract_inverted_index.detection | 10 |
| abstract_inverted_index.diagnosis | 12 |
| abstract_inverted_index.digestive | 17, 95 |
| abstract_inverted_index.effective | 23 |
| abstract_inverted_index.endoscopy | 2, 33, 62, 83, 132 |
| abstract_inverted_index.extensive | 81 |
| abstract_inverted_index.framework | 129 |
| abstract_inverted_index.highlight | 120 |
| abstract_inverted_index.model’s | 102 |
| abstract_inverted_index.potential | 122 |
| abstract_inverted_index.prevalent | 92 |
| abstract_inverted_index.promising | 118 |
| abstract_inverted_index.artefacts. | 49 |
| abstract_inverted_index.challenges | 36 |
| abstract_inverted_index.endoscopic | 110 |
| abstract_inverted_index.multiclass | 82 |
| abstract_inverted_index.showcasing | 161 |
| abstract_inverted_index.HyperKvasir | 66 |
| abstract_inverted_index.development | 21 |
| abstract_inverted_index.experiments | 99 |
| abstract_inverted_index.performance | 89 |
| abstract_inverted_index.prediction. | 115 |
| abstract_inverted_index.utilization | 54 |
| abstract_inverted_index.foundational | 128 |
| abstract_inverted_index.abnormalities | 43 |
| abstract_inverted_index.applications. | 140 |
| abstract_inverted_index.architectures | 125 |
| abstract_inverted_index.computer-aided | 131 |
| abstract_inverted_index.original-noisy | 152 |
| abstract_inverted_index.signal-tonoise | 148 |
| abstract_inverted_index.encoder-decoder | 57, 77, 124 |
| abstract_inverted_index.reconstruction. | 164 |
| abstract_inverted_index.representations | 108 |
| abstract_inverted_index.Gastrointestinal | 0 |
| abstract_inverted_index.original-reconstructed | 159 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.14997724 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |