{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Li X"],"funding":["Innovation and School Strengthening Project of Guangdong University of Science and Technology","Research Capability Enhancement Project of Guangdong University of Science and Technology","the Engineering Project of Guangdong University of Science and Technology fund"],"pagination":["e0330615"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12445547"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["20(9)"],"pubmed_abstract":["Image super-resolution reconstructs high-resolution images from low-resolution inputs. However, current single-image super-resolution techniques often struggle to capture multi-scale information and extract high-frequency details, which compromises reconstruction quality. Moreover, the prevalent feed-forward network architectures lack robust feedback mechanisms for iterative refinement and enhanced acquisition of high-frequency information. To overcome these limitations, this research develops advanced strategies for multi-scale feature extraction, fusion, and feedback in single-image super-resolution. We propose an innovative error-driven, multi-scale dense residual network (EMDN) that retains a feed-forward structure while integrating error-driven feedback. Specifically, our approach uti"],"journal":["PloS one"],"pubmed_title":["Multi-scale error-driven dense residual network for image super-resolution reconstruction."],"pmcid":["PMC12445547"],"funding_grant_id":["GKY-2024KYQNK-17","gky-2022cqtd-4","GKY-2023KYYBK-20","gky-2022kyzdk-12"],"pubmed_authors":["Liang S","Li X","Wu J","Yang L"],"additional_accession":[]},"is_claimable":false,"name":"Multi-scale error-driven dense residual network for image super-resolution reconstruction.","description":"Image super-resolution reconstructs high-resolution images from low-resolution inputs. However, current single-image super-resolution techniques often struggle to capture multi-scale information and extract high-frequency details, which compromises reconstruction quality. Moreover, the prevalent feed-forward network architectures lack robust feedback mechanisms for iterative refinement and enhanced acquisition of high-frequency information. To overcome these limitations, this research develops advanced strategies for multi-scale feature extraction, fusion, and feedback in single-image super-resolution. We propose an innovative error-driven, multi-scale dense residual network (EMDN) that retains a feed-forward structure while integrating error-driven feedback. Specifically, our approach uti","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025","modification":"2026-06-03T15:23:15.049Z","creation":"2026-04-29T03:12:00.638Z"},"accession":"S-EPMC12445547","cross_references":{"pubmed":["40966239"],"doi":["10.1371/journal.pone.0330615"]}}