Matches in SemOpenAlex for { <https://semopenalex.org/work/W3117482451> ?p ?o ?g. }
Showing items 1 to 84 of
84
with 100 items per page.
- W3117482451 abstract "Statisticians, as well as machine learning and computer vision experts, have been studying image reconstitution through denoising different domains of photography, such as textual documentation, tomographic, astronomical, and low-light photography. In this paper, we apply common inferential kernel filters in the R and python languages, as well as Adobe Lightroom's denoise filter, and compare their effectiveness in removing noise from JPEG images. We ran standard benchmark tests to evaluate each method's effectiveness for removing noise. In doing so, we also surveyed students at Elon University about their opinion of a single filtered photo from a collection of photos processed by the various filter methods. Many scientists believe that noise filters cause blurring and image quality loss so we analyzed whether or not people felt as though denoising causes any quality loss as compared to their noiseless images. Individuals assigned scores indicating the image quality of a denoised photo compared to its noiseless counterpart on a 1 to 10 scale. Survey scores are compared across filters to evaluate whether there were significant differences in image quality scores received. Benchmark scores were compared to the visual perception scores. Then, an analysis of covariance test was run to identify whether or not survey training scores explained any unplanned variation in visual scores assigned by students across the filter methods." @default.
- W3117482451 created "2021-01-05" @default.
- W3117482451 creator A5030640201 @default.
- W3117482451 creator A5039853879 @default.
- W3117482451 date "2020-12-18" @default.
- W3117482451 modified "2023-09-27" @default.
- W3117482451 title "A Survey on the Visual Perceptions of Gaussian Noise Filtering on Photography." @default.
- W3117482451 cites W1964107947 @default.
- W3117482451 cites W199564985 @default.
- W3117482451 cites W2061516586 @default.
- W3117482451 cites W2063983719 @default.
- W3117482451 cites W2097073572 @default.
- W3117482451 cites W2168631574 @default.
- W3117482451 cites W2489320132 @default.
- W3117482451 cites W2500199796 @default.
- W3117482451 cites W2507211415 @default.
- W3117482451 cites W2727642811 @default.
- W3117482451 cites W3210232381 @default.
- W3117482451 hasPublicationYear "2020" @default.
- W3117482451 type Work @default.
- W3117482451 sameAs 3117482451 @default.
- W3117482451 citedByCount "0" @default.
- W3117482451 crossrefType "posted-content" @default.
- W3117482451 hasAuthorship W3117482451A5030640201 @default.
- W3117482451 hasAuthorship W3117482451A5039853879 @default.
- W3117482451 hasConcept C106131492 @default.
- W3117482451 hasConcept C115961682 @default.
- W3117482451 hasConcept C119657128 @default.
- W3117482451 hasConcept C142362112 @default.
- W3117482451 hasConcept C153349607 @default.
- W3117482451 hasConcept C154945302 @default.
- W3117482451 hasConcept C15744967 @default.
- W3117482451 hasConcept C163294075 @default.
- W3117482451 hasConcept C169760540 @default.
- W3117482451 hasConcept C198751489 @default.
- W3117482451 hasConcept C26760741 @default.
- W3117482451 hasConcept C31972630 @default.
- W3117482451 hasConcept C41008148 @default.
- W3117482451 hasConcept C55352655 @default.
- W3117482451 hasConcept C9417928 @default.
- W3117482451 hasConcept C99498987 @default.
- W3117482451 hasConceptScore W3117482451C106131492 @default.
- W3117482451 hasConceptScore W3117482451C115961682 @default.
- W3117482451 hasConceptScore W3117482451C119657128 @default.
- W3117482451 hasConceptScore W3117482451C142362112 @default.
- W3117482451 hasConceptScore W3117482451C153349607 @default.
- W3117482451 hasConceptScore W3117482451C154945302 @default.
- W3117482451 hasConceptScore W3117482451C15744967 @default.
- W3117482451 hasConceptScore W3117482451C163294075 @default.
- W3117482451 hasConceptScore W3117482451C169760540 @default.
- W3117482451 hasConceptScore W3117482451C198751489 @default.
- W3117482451 hasConceptScore W3117482451C26760741 @default.
- W3117482451 hasConceptScore W3117482451C31972630 @default.
- W3117482451 hasConceptScore W3117482451C41008148 @default.
- W3117482451 hasConceptScore W3117482451C55352655 @default.
- W3117482451 hasConceptScore W3117482451C9417928 @default.
- W3117482451 hasConceptScore W3117482451C99498987 @default.
- W3117482451 hasLocation W31174824511 @default.
- W3117482451 hasOpenAccess W3117482451 @default.
- W3117482451 hasPrimaryLocation W31174824511 @default.
- W3117482451 hasRelatedWork W1490542379 @default.
- W3117482451 hasRelatedWork W1977725648 @default.
- W3117482451 hasRelatedWork W1991459362 @default.
- W3117482451 hasRelatedWork W2017843919 @default.
- W3117482451 hasRelatedWork W2040548061 @default.
- W3117482451 hasRelatedWork W2041159888 @default.
- W3117482451 hasRelatedWork W2075729129 @default.
- W3117482451 hasRelatedWork W2104070163 @default.
- W3117482451 hasRelatedWork W2116765143 @default.
- W3117482451 hasRelatedWork W2162915697 @default.
- W3117482451 hasRelatedWork W2527538965 @default.
- W3117482451 hasRelatedWork W2612529237 @default.
- W3117482451 hasRelatedWork W2783700464 @default.
- W3117482451 hasRelatedWork W2897941920 @default.
- W3117482451 hasRelatedWork W2942347374 @default.
- W3117482451 hasRelatedWork W2963932574 @default.
- W3117482451 hasRelatedWork W2969842256 @default.
- W3117482451 hasRelatedWork W3046648673 @default.
- W3117482451 hasRelatedWork W3098846660 @default.
- W3117482451 hasRelatedWork W3128092465 @default.
- W3117482451 isParatext "false" @default.
- W3117482451 isRetracted "false" @default.
- W3117482451 magId "3117482451" @default.
- W3117482451 workType "article" @default.