FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4367296014> ?p ?o ?g. }

• W4367296014 endingPage "15" @default.
• W4367296014 startingPage "1" @default.
• W4367296014 abstract "The image enhancement for the natural images is the vast field where the quality of the images degrades based on the capturing and processing methods employed by the capturing devices. Based on noise type and estimation of noise, filter need to be adopted for enhancing the quality of the image. In the same manner, the medical field also needs some filtering mechanism to reduce the noise and detection of the disease based on the clarity of the image captured; in accordance with it, the preprocessing steps play a vital role to reduce the burden on the radiologist to make the decision on presence of disease. Based on the estimated noise and its type, the filters are selected to delete the unwanted signals from the image. Hence, identifying noise types and denoising play an important role in image analysis. The proposed framework addresses the noise estimation and filtering process to obtain the enhanced images. This paper estimates and detects the noise types, namely Gaussian, motion artifacts, Poisson, salt-andpepper, and speckle noises. Noise is estimated by using discrete wavelet transformation (DWT). This separates the image into quadruple sub-bands. Noise and HH sub-band are high-frequency components. HH sub-band also has vertical edges. These vertical edges are removed by performing Hadamard operation on downsampled Sobel edge-detected image and HH sub-band. Using HH sub-band after removing vertical edges is considered for estimating the noise. The Rician energy equation is used to estimate the noise. This is given as input for Artificial Neural Network to improve the estimated noise level. For identifying noise type, CNN is used. After removing vertical edges, the HH sub-band is given to the CNN model for classification. The classification accuracy results of identifying noise type are 100% on natural images and 96.3% on medical images." @default.
• W4367296014 created "2023-04-29" @default.
• W4367296014 creator A5006705078 @default.
• W4367296014 creator A5021464185 @default.
• W4367296014 creator A5029488029 @default.
• W4367296014 creator A5039350782 @default.
• W4367296014 creator A5039651839 @default.
• W4367296014 creator A5076238504 @default.
• W4367296014 creator A5088118561 @default.
• W4367296014 date "2023-04-28" @default.
• W4367296014 modified "2023-09-26" @default.
• W4367296014 title "Noise Estimation and Type Identification in Natural Scene and Medical Images using Deep Learning Approaches" @default.
• W4367296014 cites W2046372571 @default.
• W4367296014 cites W2051903461 @default.
• W4367296014 cites W2055472139 @default.
• W4367296014 cites W2059784307 @default.
• W4367296014 cites W2083927153 @default.
• W4367296014 cites W2097073572 @default.
• W4367296014 cites W2109577576 @default.
• W4367296014 cites W2406389791 @default.
• W4367296014 cites W2544820280 @default.
• W4367296014 cites W2748214721 @default.
• W4367296014 cites W2770416538 @default.
• W4367296014 cites W2809621829 @default.
• W4367296014 cites W2940565608 @default.
• W4367296014 cites W2942477607 @default.
• W4367296014 cites W2955414824 @default.
• W4367296014 cites W2983610293 @default.
• W4367296014 cites W2995261518 @default.
• W4367296014 cites W3045980433 @default.
• W4367296014 cites W3047678576 @default.
• W4367296014 cites W3119741437 @default.
• W4367296014 cites W3131550923 @default.
• W4367296014 cites W3169893408 @default.
• W4367296014 cites W3204059660 @default.
• W4367296014 cites W3204917648 @default.
• W4367296014 cites W3206711565 @default.
• W4367296014 cites W4213119821 @default.
• W4367296014 cites W4213271663 @default.
• W4367296014 cites W4214605467 @default.
• W4367296014 cites W4234687630 @default.
• W4367296014 doi "https://doi.org/10.1155/2023/3923667" @default.
• W4367296014 hasPublicationYear "2023" @default.
• W4367296014 type Work @default.
• W4367296014 citedByCount "0" @default.
• W4367296014 crossrefType "journal-article" @default.
• W4367296014 hasAuthorship W4367296014A5006705078 @default.
• W4367296014 hasAuthorship W4367296014A5021464185 @default.
• W4367296014 hasAuthorship W4367296014A5029488029 @default.
• W4367296014 hasAuthorship W4367296014A5039350782 @default.
• W4367296014 hasAuthorship W4367296014A5039651839 @default.
• W4367296014 hasAuthorship W4367296014A5076238504 @default.
• W4367296014 hasAuthorship W4367296014A5088118561 @default.
• W4367296014 hasBestOaLocation W43672960141 @default.
• W4367296014 hasConcept C113660513 @default.
• W4367296014 hasConcept C115961682 @default.
• W4367296014 hasConcept C1160358 @default.
• W4367296014 hasConcept C153180895 @default.
• W4367296014 hasConcept C154945302 @default.
• W4367296014 hasConcept C163294075 @default.
• W4367296014 hasConcept C180940675 @default.
• W4367296014 hasConcept C182163834 @default.
• W4367296014 hasConcept C193536780 @default.
• W4367296014 hasConcept C200378446 @default.
• W4367296014 hasConcept C29265498 @default.
• W4367296014 hasConcept C30703548 @default.
• W4367296014 hasConcept C31972630 @default.
• W4367296014 hasConcept C35772409 @default.
• W4367296014 hasConcept C41008148 @default.
• W4367296014 hasConcept C4199805 @default.
• W4367296014 hasConcept C55352655 @default.
• W4367296014 hasConcept C9417928 @default.
• W4367296014 hasConcept C99498987 @default.
• W4367296014 hasConceptScore W4367296014C113660513 @default.
• W4367296014 hasConceptScore W4367296014C115961682 @default.
• W4367296014 hasConceptScore W4367296014C1160358 @default.
• W4367296014 hasConceptScore W4367296014C153180895 @default.
• W4367296014 hasConceptScore W4367296014C154945302 @default.
• W4367296014 hasConceptScore W4367296014C163294075 @default.
• W4367296014 hasConceptScore W4367296014C180940675 @default.
• W4367296014 hasConceptScore W4367296014C182163834 @default.
• W4367296014 hasConceptScore W4367296014C193536780 @default.
• W4367296014 hasConceptScore W4367296014C200378446 @default.
• W4367296014 hasConceptScore W4367296014C29265498 @default.
• W4367296014 hasConceptScore W4367296014C30703548 @default.
• W4367296014 hasConceptScore W4367296014C31972630 @default.
• W4367296014 hasConceptScore W4367296014C35772409 @default.
• W4367296014 hasConceptScore W4367296014C41008148 @default.
• W4367296014 hasConceptScore W4367296014C4199805 @default.
• W4367296014 hasConceptScore W4367296014C55352655 @default.
• W4367296014 hasConceptScore W4367296014C9417928 @default.
• W4367296014 hasConceptScore W4367296014C99498987 @default.
• W4367296014 hasFunder F4320323722 @default.
• W4367296014 hasLocation W43672960141 @default.
• W4367296014 hasOpenAccess W4367296014 @default.
• W4367296014 hasPrimaryLocation W43672960141 @default.
• W4367296014 hasRelatedWork W2019957195 @default.
• W4367296014 hasRelatedWork W2059394828 @default.

• next