Statistical identification and analysis of defect development in digital imagers

The lifetime of solid-state image sensors is limited by the appearance of defects, particularly hot-pixels, which we have previously shown to develop continuously over the sensor lifetime. Analysis based on spatial distribution and temporal growth of defects displayed no evidence of the defects being caused by material degradation. Instead, high radiation appears to accelerate defect development in image sensors. It is important to detect these faulty pixels prior to the use of image enhancement algorithms to avoid spreading the error to neighboring pixels. The date on which a defect has first developed can be extracted from past images. Previously, an automatic defect detection algorithm using Bayesian probability accumulation was introduced and tested. We performed extensive testing of this Bayes-based algorithm by detecting defects in image datasets obtained from four cameras. Our results have indicated that the Bayes detection scheme was able to identify all defects in these cameras with less than 3% difference from visual inspected result. In this paper, we introduce an alternative technique, the Maximum Likelihood detection algorithm, and evaluate its performance using Monte Carlo simulations based on three criterias: image exposure, defect parameters and pixel estimation. Preliminary results show that the Maximum likelihood detection algorithm is able to achieve higher accuracy than the Bayes detection algorithm, with 90% perfect detection in images captured at long exposures (>0.125s).