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Blind assessment of image blur using the Haar wavelet

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dc.contributor.author Bachoo, A
dc.date.accessioned 2012-01-16T10:56:28Z
dc.date.available 2012-01-16T10:56:28Z
dc.date.issued 2010-10
dc.identifier.citation Bachoo, A. 2010. Blind assessment of image blur using the Haar wavelet. 2010 Annual Research Conference of the South African Institute for Computer Scientists and Information Technologists (SAICSIT 2010), Bela Bela, South Africa, 11-13 October 2010, pp4 en_US
dc.identifier.uri http://hdl.handle.net/10204/5489
dc.description 2010 Annual Research Conference of the South African Institute for Computer Scientists and Information Technologists (SAICSIT 2010), Bela Bela, South Africa, 11-13 October 2010 en_US
dc.description.abstract Images and video captured in real world situations generally have distorted digital pixel values. A variety of situations can cause these image degradations: sensor motion, environmental conditions and random noise. A crucial procedure in computer vision is the assessment and quantification of digital image quality. A numerical score for describing image quality is useful for a number of applications, some of which include improving the performance of an image acquisition system and adaptive algorithms. We present an intuitive quality metric for characterizing the amount of blur in an image, through blind image assessment, using the Haar discrete wavelet transform. Thus, the method does not require a reference image or any prior information. The novelty of our method lies in processing the image derivative using the discrete wavelet transform rather than directly processing image intensity values as is traditionally done. We present late breaking results and analysis for a small set of data. The proposed method shows promise for a large number of avenues such as real- time blur level assessment and image depth of focus estimation. en_US
dc.language.iso en en_US
dc.relation.ispartofseries Workflow;4632
dc.subject Image blur characterization en_US
dc.subject Haar wavelet en_US
dc.subject Blind image quality en_US
dc.subject No-reference image en_US
dc.title Blind assessment of image blur using the Haar wavelet en_US
dc.type Conference Presentation en_US
dc.identifier.apacitation Bachoo, A. (2010). Blind assessment of image blur using the Haar wavelet. http://hdl.handle.net/10204/5489 en_ZA
dc.identifier.chicagocitation Bachoo, A. "Blind assessment of image blur using the Haar wavelet." (2010): http://hdl.handle.net/10204/5489 en_ZA
dc.identifier.vancouvercitation Bachoo A, Blind assessment of image blur using the Haar wavelet; 2010. http://hdl.handle.net/10204/5489 . en_ZA
dc.identifier.ris TY - Conference Presentation AU - Bachoo, A AB - Images and video captured in real world situations generally have distorted digital pixel values. A variety of situations can cause these image degradations: sensor motion, environmental conditions and random noise. A crucial procedure in computer vision is the assessment and quantification of digital image quality. A numerical score for describing image quality is useful for a number of applications, some of which include improving the performance of an image acquisition system and adaptive algorithms. We present an intuitive quality metric for characterizing the amount of blur in an image, through blind image assessment, using the Haar discrete wavelet transform. Thus, the method does not require a reference image or any prior information. The novelty of our method lies in processing the image derivative using the discrete wavelet transform rather than directly processing image intensity values as is traditionally done. We present late breaking results and analysis for a small set of data. The proposed method shows promise for a large number of avenues such as real- time blur level assessment and image depth of focus estimation. DA - 2010-10 DB - ResearchSpace DP - CSIR KW - Image blur characterization KW - Haar wavelet KW - Blind image quality KW - No-reference image LK - https://researchspace.csir.co.za PY - 2010 T1 - Blind assessment of image blur using the Haar wavelet TI - Blind assessment of image blur using the Haar wavelet UR - http://hdl.handle.net/10204/5489 ER - en_ZA


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