FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3012209675> ?p ?o ?g. }

• W3012209675 endingPage "5323" @default.
• W3012209675 startingPage "5310" @default.
• W3012209675 abstract "Recent works on adaptive sparse and on low-rank signal modeling have demonstrated their usefulness in various image/video processing applications. Patch-based methods exploit local patch sparsity, whereas other works apply low-rankness of grouped patches to exploit image non-local structures. However, using either approach alone usually limits performance in image reconstruction or recovery applications. In this work, we propose a simultaneous sparsity and low-rank model, dubbed STROLLR, to better represent natural images. In order to fully utilize both the local and non-local image properties, we develop an image restoration framework using a transform learning scheme with joint low-rank regularization. The approach owes some of its computational efficiency and good performance to the use of transform learning for adaptive sparse representation rather than the popular synthesis dictionary learning algorithms, which involve approximation of NP-hard sparse coding and expensive learning steps. We demonstrate the proposed framework in various applications to image denoising, inpainting, and compressed sensing based magnetic resonance imaging. Results show promising performance compared to state-of-the-art competing methods." @default.
• W3012209675 created "2020-03-23" @default.
• W3012209675 creator A5006530807 @default.
• W3012209675 creator A5024709593 @default.
• W3012209675 creator A5026523226 @default.
• W3012209675 date "2020-01-01" @default.
• W3012209675 modified "2023-10-17" @default.
• W3012209675 title "Image Recovery via Transform Learning and Low-Rank Modeling: The Power of Complementary Regularizers" @default.
• W3012209675 cites W1562968274 @default.
• W3012209675 cites W1564864701 @default.
• W3012209675 cites W1829781029 @default.
• W3012209675 cites W1906770428 @default.
• W3012209675 cites W1968691112 @default.
• W3012209675 cites W1972499291 @default.
• W3012209675 cites W1978418556 @default.
• W3012209675 cites W1978749115 @default.
• W3012209675 cites W1994281301 @default.
• W3012209675 cites W2006262236 @default.
• W3012209675 cites W2008357395 @default.
• W3012209675 cites W2014311222 @default.
• W3012209675 cites W2017399967 @default.
• W3012209675 cites W2018784354 @default.
• W3012209675 cites W2018990310 @default.
• W3012209675 cites W2021302824 @default.
• W3012209675 cites W2028762251 @default.
• W3012209675 cites W2029411311 @default.
• W3012209675 cites W2029816571 @default.
• W3012209675 cites W2037642501 @default.
• W3012209675 cites W2042220165 @default.
• W3012209675 cites W2045079989 @default.
• W3012209675 cites W2045737896 @default.
• W3012209675 cites W2056370875 @default.
• W3012209675 cites W2062811295 @default.
• W3012209675 cites W2066771519 @default.
• W3012209675 cites W2075157914 @default.
• W3012209675 cites W2090457307 @default.
• W3012209675 cites W2096461046 @default.
• W3012209675 cites W2100415658 @default.
• W3012209675 cites W2101675075 @default.
• W3012209675 cites W2103559027 @default.
• W3012209675 cites W2109240917 @default.
• W3012209675 cites W2111557737 @default.
• W3012209675 cites W2128659236 @default.
• W3012209675 cites W2130184048 @default.
• W3012209675 cites W2130975789 @default.
• W3012209675 cites W2141168890 @default.
• W3012209675 cites W2142224912 @default.
• W3012209675 cites W2147573997 @default.
• W3012209675 cites W2153663612 @default.
• W3012209675 cites W2153703004 @default.
• W3012209675 cites W2159766410 @default.
• W3012209675 cites W2160547390 @default.
• W3012209675 cites W2167506279 @default.
• W3012209675 cites W2167577940 @default.
• W3012209675 cites W2168668658 @default.
• W3012209675 cites W2172275395 @default.
• W3012209675 cites W2176563895 @default.
• W3012209675 cites W2189938900 @default.
• W3012209675 cites W2203454327 @default.
• W3012209675 cites W2203654268 @default.
• W3012209675 cites W2207282238 @default.
• W3012209675 cites W2293090580 @default.
• W3012209675 cites W2295936755 @default.
• W3012209675 cites W2489519930 @default.
• W3012209675 cites W2505029951 @default.
• W3012209675 cites W2508457857 @default.
• W3012209675 cites W2512984726 @default.
• W3012209675 cites W2536599074 @default.
• W3012209675 cites W2551161082 @default.
• W3012209675 cites W2614367549 @default.
• W3012209675 cites W2641816967 @default.
• W3012209675 cites W2763211003 @default.
• W3012209675 cites W2777802649 @default.
• W3012209675 cites W2779105280 @default.
• W3012209675 cites W2963129783 @default.
• W3012209675 cites W2963294657 @default.
• W3012209675 cites W2963315679 @default.
• W3012209675 cites W3102562067 @default.
• W3012209675 cites W3105699558 @default.
• W3012209675 cites W3105700508 @default.
• W3012209675 cites W3144890709 @default.
• W3012209675 cites W4235713725 @default.
• W3012209675 cites W4292363360 @default.
• W3012209675 doi "https://doi.org/10.1109/tip.2020.2980753" @default.
• W3012209675 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/32203020" @default.
• W3012209675 hasPublicationYear "2020" @default.
• W3012209675 type Work @default.
• W3012209675 sameAs 3012209675 @default.
• W3012209675 citedByCount "41" @default.
• W3012209675 countsByYear W30122096752019 @default.
• W3012209675 countsByYear W30122096752020 @default.
• W3012209675 countsByYear W30122096752021 @default.
• W3012209675 countsByYear W30122096752022 @default.
• W3012209675 countsByYear W30122096752023 @default.
• W3012209675 crossrefType "journal-article" @default.
• W3012209675 hasAuthorship W3012209675A5006530807 @default.
• W3012209675 hasAuthorship W3012209675A5024709593 @default.
• W3012209675 hasAuthorship W3012209675A5026523226 @default.

• next