Abstract

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 …

Date

September 12, 2025

Authors

Sreetama Sarkar, Xinan Ye, Gourav Datta, Peter A Beerel

Conference

International Conference on Pattern Recognition

Pages

441-455

Publisher

Springer, Cham