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.