FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3217451768> ?p ?o ?g. }

• W3217451768 endingPage "266" @default.
• W3217451768 startingPage "248" @default.
• W3217451768 abstract "A non-convex ternary variational decomposition model is proposed in this study, which decomposes the image into three components including structure, texture and noise. In the model, a non-convex total variation (NTV) regulariser is utilised to model the structure component, and the weaker G and E spaces are used to model the texture and noise components, respectively. The proposed model provides a very sparse representation of the structure in total variation (TV) transform domain due to the use of non-convex regularisation and cleanly separates the texture and noise since two different weaker spaces are used to model these two components, respectively. In image denoising application, the proposed model can successfully remove noise while effectively preserving image edges and constructing textures. An alternating direction iteration algorithm combining with iteratively reweighted l1 algorithm, projection algorithm and wavelet soft threshold algorithm is introduced to effectively solve the proposed model. Numerical results validate the model and the algorithm for both synthetic and real images. Furthermore, compared with several state-of-the-art image variational restoration models, the proposed model yields the best performance in terms of the peak signal to noise ratio (PSNR) and the mean structural similarity index (SSIM)." @default.
• W3217451768 created "2021-12-06" @default.
• W3217451768 creator A5014785715 @default.
• W3217451768 creator A5015106313 @default.
• W3217451768 creator A5056788729 @default.
• W3217451768 creator A5078855064 @default.
• W3217451768 date "2021-11-29" @default.
• W3217451768 modified "2023-10-11" @default.
• W3217451768 title "A non‐convex ternary variational decomposition and its application for image denoising" @default.
• W3217451768 cites W1488881187 @default.
• W3217451768 cites W1880154232 @default.
• W3217451768 cites W1992950686 @default.
• W3217451768 cites W1998999283 @default.
• W3217451768 cites W2005140191 @default.
• W3217451768 cites W2006206662 @default.
• W3217451768 cites W2016670111 @default.
• W3217451768 cites W2020989074 @default.
• W3217451768 cites W2038457848 @default.
• W3217451768 cites W2039158956 @default.
• W3217451768 cites W2040378863 @default.
• W3217451768 cites W2045354372 @default.
• W3217451768 cites W2049984505 @default.
• W3217451768 cites W2060653583 @default.
• W3217451768 cites W2060945009 @default.
• W3217451768 cites W2064556594 @default.
• W3217451768 cites W2066630786 @default.
• W3217451768 cites W2074406023 @default.
• W3217451768 cites W2077051484 @default.
• W3217451768 cites W2084293828 @default.
• W3217451768 cites W2085411090 @default.
• W3217451768 cites W2103559027 @default.
• W3217451768 cites W2133665775 @default.
• W3217451768 cites W2135411024 @default.
• W3217451768 cites W2152254169 @default.
• W3217451768 cites W2166388529 @default.
• W3217451768 cites W2201308850 @default.
• W3217451768 cites W2294876837 @default.
• W3217451768 cites W2497853665 @default.
• W3217451768 cites W2508457857 @default.
• W3217451768 cites W2748967439 @default.
• W3217451768 cites W2750681454 @default.
• W3217451768 cites W2751011928 @default.
• W3217451768 cites W2772548996 @default.
• W3217451768 cites W2782411500 @default.
• W3217451768 cites W2887867441 @default.
• W3217451768 cites W2903018872 @default.
• W3217451768 cites W2903984188 @default.
• W3217451768 cites W2907879453 @default.
• W3217451768 cites W2911457000 @default.
• W3217451768 cites W2913127649 @default.
• W3217451768 cites W2919702051 @default.
• W3217451768 cites W2947057225 @default.
• W3217451768 cites W2953843381 @default.
• W3217451768 cites W2979058469 @default.
• W3217451768 cites W2989815236 @default.
• W3217451768 cites W3007040424 @default.
• W3217451768 cites W3009016150 @default.
• W3217451768 cites W3010678772 @default.
• W3217451768 cites W3016471017 @default.
• W3217451768 cites W3029326004 @default.
• W3217451768 cites W3042523595 @default.
• W3217451768 cites W3099748020 @default.
• W3217451768 cites W3132849585 @default.
• W3217451768 cites W3159406499 @default.
• W3217451768 cites W2949581848 @default.
• W3217451768 doi "https://doi.org/10.1049/sil2.12088" @default.
• W3217451768 hasPublicationYear "2021" @default.
• W3217451768 type Work @default.
• W3217451768 sameAs 3217451768 @default.
• W3217451768 citedByCount "2" @default.
• W3217451768 countsByYear W32174517682022 @default.
• W3217451768 countsByYear W32174517682023 @default.
• W3217451768 crossrefType "journal-article" @default.
• W3217451768 hasAuthorship W3217451768A5014785715 @default.
• W3217451768 hasAuthorship W3217451768A5015106313 @default.
• W3217451768 hasAuthorship W3217451768A5056788729 @default.
• W3217451768 hasAuthorship W3217451768A5078855064 @default.
• W3217451768 hasConcept C11413529 @default.
• W3217451768 hasConcept C115961682 @default.
• W3217451768 hasConcept C153180895 @default.
• W3217451768 hasConcept C154945302 @default.
• W3217451768 hasConcept C163294075 @default.
• W3217451768 hasConcept C33923547 @default.
• W3217451768 hasConcept C41008148 @default.
• W3217451768 hasConcept C47432892 @default.
• W3217451768 hasConcept C57493831 @default.
• W3217451768 hasConcept C99498987 @default.
• W3217451768 hasConceptScore W3217451768C11413529 @default.
• W3217451768 hasConceptScore W3217451768C115961682 @default.
• W3217451768 hasConceptScore W3217451768C153180895 @default.
• W3217451768 hasConceptScore W3217451768C154945302 @default.
• W3217451768 hasConceptScore W3217451768C163294075 @default.
• W3217451768 hasConceptScore W3217451768C33923547 @default.
• W3217451768 hasConceptScore W3217451768C41008148 @default.
• W3217451768 hasConceptScore W3217451768C47432892 @default.
• W3217451768 hasConceptScore W3217451768C57493831 @default.
• W3217451768 hasConceptScore W3217451768C99498987 @default.
• W3217451768 hasFunder F4320321001 @default.

• next