Efficient defect pixel cluster detection and correction for Bayer CFA image sequences

Touraj Tajbakhsh

Abstract: Image sensor arrays may have defect pixels, either originating from manufacturing or being developed over the lifetime of the image sensor array. Continuous defect pixel detection and correction performing during camera runtime is desirable. On-the-fly detection and correction is challenging since edges and high-frequency image content might get identified as defect pixel regions and intact pixels become corrupted during defect pixel replacement. We propose a table-based detection and correction method which by and by fills the non-volatile table during normal camera operation. In this work we model defect pixels and pixel clusters to be stuck to fixed values or at least fixed to a narrow value range whereas the local neighborhood of these pixels indicate a normal behavior. The idea is to temporally observe the value ranges of small group of pixels (e.g. 4x4 pixel blocks) and to decide about their defective condition depending on their variability with respect to their neighbor pixels. Our method is computationally efficient, requires no frame buffer, requires modest memory, and therefore is appropriate to operate in line-buffer based image signal processing (ISP) systems. Our results indicate high reliability in terms of detection rates and robustness against high-frequency image content. As part of the defect pixel replacement system we also propose a simple and efficient defect pixel correction method based on the mean of medians operating on the Bayer CFA image domain.