20 February 2007 Kernel-size selection for defect pixel identification and correction
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Abstract
In a digital imaging system, the Image Signal Processing (ISP) pipeline may be called on to identify and hide defective pixels in the image sensor. Often filters are designed and implemented to accomplish these tasks without considering the cost in memory or the effect on actual images. We have created a simulation system which uses an inverse ISP model to add defect pixels to raw sensor data. The simulation includes lens blur, inverse gamma, additive white noise, and mosaic. Defect pixels are added to the simulated raw image, which is then processed by various defect pixel correction algorithms. The end result is compared against the original simulated raw data to measure the effect of the added defects and defect pixel correction. We have implemented a bounding min-max filter as our defect pixel correction algorithm. The simulations show that the choice of kernel size and other parameters depends not only on memory constraints, but also on the defect pixel rate. At high defect pixel rates, algorithms with more aggressive defect correction are more effective, but also result in higher accidental degradation.
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Edward Chang, "Kernel-size selection for defect pixel identification and correction", Proc. SPIE 6502, Digital Photography III, 65020J (20 February 2007); doi: 10.1117/12.705383; https://doi.org/10.1117/12.705383
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