Defective pixels degrade the quality of the images produced by digital imagers. If those pixels are not corrected early in the image processing pipeline, demosaicing and filtering operations will cause them to spread and appear as colored clusters that are detrimental to image quality. This paper presents a robust defect pixel detection and correction solution for Bayer imaging systems. The detection mechanism is designed to robustly identify singlets and couplets of hot pixel, cold pixels or mixture of both types, and results in high defect detection rates. The correction mechanism is designed to be detail-preserving and robust to false positives, and results in high image quality. Both mechanisms are computationally cheap and easy to tune. Experimental results demonstrate the aforementioned merits as well as the solution outperformance of conventional correction methods.
Noha El-Yamany, "Robust Defect Pixel Detection and Correction for Bayer Imaging Systems" in Proc. IS&T Int’l. Symp. on Electronic Imaging: Digital Photography and Mobile Imaging XIII, 2017, pp 46 - 51, https://doi.org/10.2352/ISSN.2470-1173.2017.15.DPMI-088