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Please use this identifier to cite or link to this item: http://hdl.handle.net/2328/25732

Title: Adaptive order-statistics multi-shell filtering for bad pixel correction within CFA demosaicking
Authors: Li, Jimmy Siu
Randhawa, Sharmil
Keywords: Bad pixel correction
CFA demosaicking
Adaptive order statistics
Multi-shell filtering
Issue Date: 2009
Publisher: Institute of Electrical and Electronics Engineers Computer Society (IEEE Publishing)
Citation: Li, J. and Randhawa, S. 2009. Adaptive order-statistics multi-shell filtering for bad pixel correction within CFA demosaicking. 2009 IEEE Region 10 Conference (TENCON 2009), 1-6.
Abstract: As today's digital cameras contain millions of image sensors, it is highly probable that the image sensors will contain a few defective pixels due to errors in the fabrication process. While these bad pixels would normally be mapped out in the manufacturing process, more defective pixels, known as hot pixels, could appear over time with camera usage. Since some hot pixels can still function at normal settings, they need not be permanently mapped out because they will only appear on a long exposure and/or at high ISO settings. In this paper, we apply an adaptive order-statistics multi-shell filter within CFA demosaicking to filter out only bad pixels whilst preserving the rest of the image. The CFA image containing bad pixels is first demosaicked to produce a full colour image. The adaptive filter is then only applied to the actual sensor pixels within the colour image for bad pixel correction. Demosaicking is then re-applied at those bad pixel locations to produce the final full colour image free of defective pixels. It has been shown that our proposed method outperforms a separate process of CFA demosaicking followed by bad pixel removal.
URI: http://hdl.handle.net/2328/25732
ISSN: 9781424445462
Appears in Collections:Computer Science, Engineering and Mathematics - Collected Works

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