Matches in SemOpenAlex for { <https://semopenalex.org/work/W2943254484> ?p ?o ?g. }
Showing items 1 to 96 of
96
with 100 items per page.
- W2943254484 abstract "In the Multimedia era, removal of the Noises from an image becomes a key challenge in the field of Digital Image Processing (DIP) and Computer Vision. Noise may be mixed with an image during capturing time, transmission time or due to dust particle on the screen of capturing device. Therefore, removal of these unwanted signals from the image is urgently required for the better analysis of the image and the de-noised image is more meaningful for Object detection, Edge detection and many more. There are various types of image noise, however, Gaussian Noise and Impulse Noise are commonly found in the image. This work focuses on the outliers and Mean Filter to improve the performance for Gaussian noise reduction from the image. In experimental assessments, artificial noise has been mixed using MATLAB to MSRA (10k images) dataset, this dataset is used to evaluate our proposed technique. The experiment results show that the proposed approach improves the performance in noise reduction over other filter approaches." @default.
- W2943254484 created "2019-05-09" @default.
- W2943254484 creator A5049407192 @default.
- W2943254484 creator A5073621919 @default.
- W2943254484 creator A5080920058 @default.
- W2943254484 creator A5082851516 @default.
- W2943254484 date "2018-12-01" @default.
- W2943254484 modified "2023-10-16" @default.
- W2943254484 title "An efficient Gaussian Noise Reduction Technique For Noisy Images using optimized filter approach" @default.
- W2943254484 cites W1987611495 @default.
- W2943254484 cites W1996770283 @default.
- W2943254484 cites W2083908585 @default.
- W2943254484 cites W2104275258 @default.
- W2943254484 cites W2105605631 @default.
- W2943254484 cites W2113635595 @default.
- W2943254484 cites W2146702772 @default.
- W2943254484 cites W2342450965 @default.
- W2943254484 cites W2345782852 @default.
- W2943254484 cites W2501089732 @default.
- W2943254484 cites W2603823201 @default.
- W2943254484 cites W2746923101 @default.
- W2943254484 cites W2774252572 @default.
- W2943254484 cites W2777598052 @default.
- W2943254484 cites W2794558093 @default.
- W2943254484 cites W2799134053 @default.
- W2943254484 cites W2800563186 @default.
- W2943254484 cites W2803606114 @default.
- W2943254484 cites W2805046202 @default.
- W2943254484 cites W2808225117 @default.
- W2943254484 cites W2809913882 @default.
- W2943254484 cites W2887755027 @default.
- W2943254484 doi "https://doi.org/10.1109/icsccc.2018.8703305" @default.
- W2943254484 hasPublicationYear "2018" @default.
- W2943254484 type Work @default.
- W2943254484 sameAs 2943254484 @default.
- W2943254484 citedByCount "11" @default.
- W2943254484 countsByYear W29432544842019 @default.
- W2943254484 countsByYear W29432544842021 @default.
- W2943254484 countsByYear W29432544842022 @default.
- W2943254484 countsByYear W29432544842023 @default.
- W2943254484 crossrefType "proceedings-article" @default.
- W2943254484 hasAuthorship W2943254484A5049407192 @default.
- W2943254484 hasAuthorship W2943254484A5073621919 @default.
- W2943254484 hasAuthorship W2943254484A5080920058 @default.
- W2943254484 hasAuthorship W2943254484A5082851516 @default.
- W2943254484 hasConcept C113660513 @default.
- W2943254484 hasConcept C115961682 @default.
- W2943254484 hasConcept C1160358 @default.
- W2943254484 hasConcept C127372701 @default.
- W2943254484 hasConcept C153180895 @default.
- W2943254484 hasConcept C154945302 @default.
- W2943254484 hasConcept C160633673 @default.
- W2943254484 hasConcept C163294075 @default.
- W2943254484 hasConcept C182163834 @default.
- W2943254484 hasConcept C29265498 @default.
- W2943254484 hasConcept C31972630 @default.
- W2943254484 hasConcept C35772409 @default.
- W2943254484 hasConcept C41008148 @default.
- W2943254484 hasConcept C4199805 @default.
- W2943254484 hasConcept C55352655 @default.
- W2943254484 hasConcept C9417928 @default.
- W2943254484 hasConcept C99498987 @default.
- W2943254484 hasConceptScore W2943254484C113660513 @default.
- W2943254484 hasConceptScore W2943254484C115961682 @default.
- W2943254484 hasConceptScore W2943254484C1160358 @default.
- W2943254484 hasConceptScore W2943254484C127372701 @default.
- W2943254484 hasConceptScore W2943254484C153180895 @default.
- W2943254484 hasConceptScore W2943254484C154945302 @default.
- W2943254484 hasConceptScore W2943254484C160633673 @default.
- W2943254484 hasConceptScore W2943254484C163294075 @default.
- W2943254484 hasConceptScore W2943254484C182163834 @default.
- W2943254484 hasConceptScore W2943254484C29265498 @default.
- W2943254484 hasConceptScore W2943254484C31972630 @default.
- W2943254484 hasConceptScore W2943254484C35772409 @default.
- W2943254484 hasConceptScore W2943254484C41008148 @default.
- W2943254484 hasConceptScore W2943254484C4199805 @default.
- W2943254484 hasConceptScore W2943254484C55352655 @default.
- W2943254484 hasConceptScore W2943254484C9417928 @default.
- W2943254484 hasConceptScore W2943254484C99498987 @default.
- W2943254484 hasLocation W29432544841 @default.
- W2943254484 hasOpenAccess W2943254484 @default.
- W2943254484 hasPrimaryLocation W29432544841 @default.
- W2943254484 hasRelatedWork W1969252538 @default.
- W2943254484 hasRelatedWork W2052857611 @default.
- W2943254484 hasRelatedWork W2140759374 @default.
- W2943254484 hasRelatedWork W2765624991 @default.
- W2943254484 hasRelatedWork W2980586888 @default.
- W2943254484 hasRelatedWork W3190667789 @default.
- W2943254484 hasRelatedWork W4214680952 @default.
- W2943254484 hasRelatedWork W4285137764 @default.
- W2943254484 hasRelatedWork W2181469598 @default.
- W2943254484 hasRelatedWork W2810312662 @default.
- W2943254484 isParatext "false" @default.
- W2943254484 isRetracted "false" @default.
- W2943254484 magId "2943254484" @default.
- W2943254484 workType "article" @default.