Matches in SemOpenAlex for { <https://semopenalex.org/work/W4285070320> ?p ?o ?g. }
Showing items 1 to 100 of
100
with 100 items per page.
- W4285070320 endingPage "5" @default.
- W4285070320 startingPage "1" @default.
- W4285070320 abstract "We propose a hybrid algorithm for despeckling the Synthetic Aperture Radar (SAR) images using the Convolutional Neural Network (CNN) denoising and complex wavelet shrinkage. In particular, we perform the speckle reduction process in the complex wavelet domain. We first despeckled the approximation complex wavelet coefficients using the MUltichannel LOgarithm with the Gaussian denoising algorithm (MuLoG) based on a pre-trained CNN model named FFDNet. Next, we despeckled the log-transformed details of the complex wavelet coefficients using the averaged version of the Maximum a Posteriori (AMAP) estimator. The experimental results on simulated and real SAR images showed that the proposed method achieved better speckle suppression in the homogeneous areas while preserving edges and point targets than other state-of-the-art methods." @default.
- W4285070320 created "2022-07-13" @default.
- W4285070320 creator A5003315831 @default.
- W4285070320 creator A5004494826 @default.
- W4285070320 creator A5057547054 @default.
- W4285070320 creator A5063724972 @default.
- W4285070320 date "2022-01-01" @default.
- W4285070320 modified "2023-09-30" @default.
- W4285070320 title "SAR Despeckling Based on CNN and Bayesian Estimator in Complex Wavelet Domain" @default.
- W4285070320 cites W2004376198 @default.
- W4285070320 cites W2079071390 @default.
- W4285070320 cites W2110206939 @default.
- W4285070320 cites W2133954466 @default.
- W4285070320 cites W2163403908 @default.
- W4285070320 cites W2164911019 @default.
- W4285070320 cites W2548712274 @default.
- W4285070320 cites W2929378800 @default.
- W4285070320 cites W2936355499 @default.
- W4285070320 cites W2963583038 @default.
- W4285070320 cites W3013302728 @default.
- W4285070320 cites W3037578234 @default.
- W4285070320 cites W3104725225 @default.
- W4285070320 cites W3105425562 @default.
- W4285070320 cites W3132437634 @default.
- W4285070320 cites W3136138438 @default.
- W4285070320 cites W3200726353 @default.
- W4285070320 cites W4242059867 @default.
- W4285070320 doi "https://doi.org/10.1109/lgrs.2022.3185557" @default.
- W4285070320 hasPublicationYear "2022" @default.
- W4285070320 type Work @default.
- W4285070320 citedByCount "2" @default.
- W4285070320 countsByYear W42850703202023 @default.
- W4285070320 crossrefType "journal-article" @default.
- W4285070320 hasAuthorship W4285070320A5003315831 @default.
- W4285070320 hasAuthorship W4285070320A5004494826 @default.
- W4285070320 hasAuthorship W4285070320A5057547054 @default.
- W4285070320 hasAuthorship W4285070320A5063724972 @default.
- W4285070320 hasConcept C102290492 @default.
- W4285070320 hasConcept C102592046 @default.
- W4285070320 hasConcept C105795698 @default.
- W4285070320 hasConcept C11413529 @default.
- W4285070320 hasConcept C153180895 @default.
- W4285070320 hasConcept C154945302 @default.
- W4285070320 hasConcept C155777637 @default.
- W4285070320 hasConcept C163294075 @default.
- W4285070320 hasConcept C165646398 @default.
- W4285070320 hasConcept C180940675 @default.
- W4285070320 hasConcept C185429906 @default.
- W4285070320 hasConcept C191393472 @default.
- W4285070320 hasConcept C196216189 @default.
- W4285070320 hasConcept C2777885455 @default.
- W4285070320 hasConcept C33923547 @default.
- W4285070320 hasConcept C41008148 @default.
- W4285070320 hasConcept C47432892 @default.
- W4285070320 hasConcept C49781872 @default.
- W4285070320 hasConcept C73339587 @default.
- W4285070320 hasConcept C81363708 @default.
- W4285070320 hasConcept C87360688 @default.
- W4285070320 hasConcept C9810830 @default.
- W4285070320 hasConceptScore W4285070320C102290492 @default.
- W4285070320 hasConceptScore W4285070320C102592046 @default.
- W4285070320 hasConceptScore W4285070320C105795698 @default.
- W4285070320 hasConceptScore W4285070320C11413529 @default.
- W4285070320 hasConceptScore W4285070320C153180895 @default.
- W4285070320 hasConceptScore W4285070320C154945302 @default.
- W4285070320 hasConceptScore W4285070320C155777637 @default.
- W4285070320 hasConceptScore W4285070320C163294075 @default.
- W4285070320 hasConceptScore W4285070320C165646398 @default.
- W4285070320 hasConceptScore W4285070320C180940675 @default.
- W4285070320 hasConceptScore W4285070320C185429906 @default.
- W4285070320 hasConceptScore W4285070320C191393472 @default.
- W4285070320 hasConceptScore W4285070320C196216189 @default.
- W4285070320 hasConceptScore W4285070320C2777885455 @default.
- W4285070320 hasConceptScore W4285070320C33923547 @default.
- W4285070320 hasConceptScore W4285070320C41008148 @default.
- W4285070320 hasConceptScore W4285070320C47432892 @default.
- W4285070320 hasConceptScore W4285070320C49781872 @default.
- W4285070320 hasConceptScore W4285070320C73339587 @default.
- W4285070320 hasConceptScore W4285070320C81363708 @default.
- W4285070320 hasConceptScore W4285070320C87360688 @default.
- W4285070320 hasConceptScore W4285070320C9810830 @default.
- W4285070320 hasLocation W42850703201 @default.
- W4285070320 hasOpenAccess W4285070320 @default.
- W4285070320 hasPrimaryLocation W42850703201 @default.
- W4285070320 hasRelatedWork W1557900431 @default.
- W4285070320 hasRelatedWork W1577789985 @default.
- W4285070320 hasRelatedWork W1969567648 @default.
- W4285070320 hasRelatedWork W1975415994 @default.
- W4285070320 hasRelatedWork W2129282069 @default.
- W4285070320 hasRelatedWork W2158757538 @default.
- W4285070320 hasRelatedWork W2169450829 @default.
- W4285070320 hasRelatedWork W2384830449 @default.
- W4285070320 hasRelatedWork W2792520941 @default.
- W4285070320 hasRelatedWork W2183577172 @default.
- W4285070320 hasVolume "19" @default.
- W4285070320 isParatext "false" @default.
- W4285070320 isRetracted "false" @default.
- W4285070320 workType "article" @default.