FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3197249242> ?p ?o ?g. }

• W3197249242 endingPage "8150" @default.
• W3197249242 startingPage "8150" @default.
• W3197249242 abstract "Magnetic resonance (MR) images can detect small pathological tissue with the size of 3–5 image pixels at an early stage, which is of great significance in the localization of pathological lesions and the diagnosis of disease. High-resolution MR images can provide clearer structural details and help doctors to analyze and diagnose the disease correctly. In this paper, MR super-resolution based on the multiple optimizations-based Enhanced Super Resolution Feed Back Network (ESRFBN) is proposed. The method realizes network optimization from the three perspectives of network structure, data characteristics and heterogeneous network integration. Firstly, a super-resolution network structure based on multi-slice input optimization is proposed to make full use of the structural similarity between samples. Secondly, aiming at the problem that the L1 or L2 loss function is based on a per-pixel comparison of differences, without considering human visual perception, the optimization method of multiple loss function cascade is proposed, which combines the L1 loss function to retain the color and brightness characteristics and the MS-SSIM loss function to retain the contrast characteristics of the high-frequency region better, so that the depth model has better characterization performance; thirdly, in view of the problem that large deep learning networks are difficult to balance model complexity and training difficulty, a heterogeneous network fusion method is proposed. For multiple independent deep super-resolution networks, the output of a single network is integrated through an additional fusion layer, which broadens the width of the network, and can effectively improve the mapping and characterization capabilities of high- and low-resolution features. The experimental results on two super-resolution scales and on MR images datasets of four human body parts show that the proposed large-sample space learning super-resolution method effectively improves the super-resolution performance." @default.
• W3197249242 created "2021-09-13" @default.
• W3197249242 creator A5019810689 @default.
• W3197249242 creator A5020141908 @default.
• W3197249242 creator A5027581587 @default.
• W3197249242 creator A5050960882 @default.
• W3197249242 date "2021-09-02" @default.
• W3197249242 modified "2023-10-06" @default.
• W3197249242 title "Multiple Optimizations-Based ESRFBN Super-Resolution Network Algorithm for MR Images" @default.
• W3197249242 cites W1885185971 @default.
• W3197249242 cites W2058523468 @default.
• W3197249242 cites W2087380704 @default.
• W3197249242 cites W2102772704 @default.
• W3197249242 cites W2121058967 @default.
• W3197249242 cites W2157494358 @default.
• W3197249242 cites W2562637781 @default.
• W3197249242 cites W2743628679 @default.
• W3197249242 cites W2978392370 @default.
• W3197249242 cites W3012570989 @default.
• W3197249242 cites W3013529009 @default.
• W3197249242 cites W3173621434 @default.
• W3197249242 doi "https://doi.org/10.3390/app11178150" @default.
• W3197249242 hasPublicationYear "2021" @default.
• W3197249242 type Work @default.
• W3197249242 sameAs 3197249242 @default.
• W3197249242 citedByCount "1" @default.
• W3197249242 countsByYear W31972492422023 @default.
• W3197249242 crossrefType "journal-article" @default.
• W3197249242 hasAuthorship W3197249242A5019810689 @default.
• W3197249242 hasAuthorship W3197249242A5020141908 @default.
• W3197249242 hasAuthorship W3197249242A5027581587 @default.
• W3197249242 hasAuthorship W3197249242A5050960882 @default.
• W3197249242 hasBestOaLocation W31972492421 @default.
• W3197249242 hasConcept C11413529 @default.
• W3197249242 hasConcept C153180895 @default.
• W3197249242 hasConcept C154945302 @default.
• W3197249242 hasConcept C160633673 @default.
• W3197249242 hasConcept C31972630 @default.
• W3197249242 hasConcept C41008148 @default.
• W3197249242 hasConceptScore W3197249242C11413529 @default.
• W3197249242 hasConceptScore W3197249242C153180895 @default.
• W3197249242 hasConceptScore W3197249242C154945302 @default.
• W3197249242 hasConceptScore W3197249242C160633673 @default.
• W3197249242 hasConceptScore W3197249242C31972630 @default.
• W3197249242 hasConceptScore W3197249242C41008148 @default.
• W3197249242 hasFunder F4320321001 @default.
• W3197249242 hasIssue "17" @default.
• W3197249242 hasLocation W31972492421 @default.
• W3197249242 hasLocation W31972492422 @default.
• W3197249242 hasOpenAccess W3197249242 @default.
• W3197249242 hasPrimaryLocation W31972492421 @default.
• W3197249242 hasRelatedWork W121273120 @default.
• W3197249242 hasRelatedWork W2002009170 @default.
• W3197249242 hasRelatedWork W2034462085 @default.
• W3197249242 hasRelatedWork W2090093270 @default.
• W3197249242 hasRelatedWork W2152301642 @default.
• W3197249242 hasRelatedWork W2337415362 @default.
• W3197249242 hasRelatedWork W2740820121 @default.
• W3197249242 hasRelatedWork W3005455252 @default.
• W3197249242 hasRelatedWork W317572212 @default.
• W3197249242 hasRelatedWork W4312857205 @default.
• W3197249242 hasVolume "11" @default.
• W3197249242 isParatext "false" @default.
• W3197249242 isRetracted "false" @default.
• W3197249242 magId "3197249242" @default.
• W3197249242 workType "article" @default.