  Efficient and effective on-line detection and correction of bad-pixels can
improve yield and increase the expected lifetime of image sensors. This paper
presents a comprehensive Deep Learning (DL) based on-line detection and
correction approach, suitable for a wide range of pixel corruption rates. A
confidence calibrated segmentation approach is introduced, which achieves
nearly perfect bad pixel detection, even with a few training samples. A
computationally light-weight correction algorithm is proposed for low rates of
pixel corruption, that surpasses the accuracy of traditional
interpolation-based techniques. In addition, a vision transformer (ViT)
auto-encoder based image reconstruction approach is presented which yields
promising results for high rates of pixel corruption or clustered defects.
Unlike previous methods, which use proprietary images, we demonstrate the
efficacy of the proposed methods on the open-source Samsung S7 ISP and
MIT-Adobe FiveK datasets. Our approaches yield up to 99.6% detection accuracy
with <0.6% false positives and corrected images within 1.5% average pixel error
from 70% corrupted images. We achieve correction error at par with the
state-of-the-art (SoTA) DL methods for clustered defects with less than half
the computational cost.
