Detection and correction of image distortions induced by parasitic light sensitivity using deep learning methods

Sara Esam Azmi; Chris Bartels; Zille Hussnain; J. A. P. Guelen; Egor Bondarev

doi:10.1117/12.2679501

7 June 2023 Detection and correction of image distortions induced by parasitic light sensitivity using deep learning methods

Sara Esam Azmi, Chris Bartels, Zille Hussnain, J. A. P. Guelen, Egor Bondarev

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Proceedings Volume 12701, Fifteenth International Conference on Machine Vision (ICMV 2022); 127010V (2023) https://doi.org/10.1117/12.2679501
Event: Fifteenth International Conference on Machine Vision (ICMV 2022), 2022, Rome, Italy

Abstract

Images captured by mobile camera systems are subject to distortions that can be irreversible. Sources of these distortions vary and can be attributed to sensor imperfections, lens defects, or shutter inefficiency. One form of image distortion is associated with high Parasitic-Light-Sensitivity (PLS) in CMOS Image Sensors when combined with Global Shutters (GS-CIS) in a moving camera system. The resulting distortion appears as widespread semi-transparent purple artifacts, or a complex purple fringe, covering a large area in the scene around high-intensity regions. Most of the earlier approaches addressing the purple fringing problems have been directed towards the simplest forms of this distortion and rely on heuristic image processing algorithms. Recently, machine learning methods have shown remarkable success in many image restoration and object detection problems. Nevertheless, they have not been applied for the complex purple fringing detection or correction. In this paper, we present our exploration and deployment of deep learning algorithms in a pipeline for the detection and correction of the purple fringing induced by high-PLS GS-CIS sensors. Experiments show that the proposed methods outperform state-of-the-art approaches for both problems of detection and color restoration. We achieve a final MS-SSIM of 0.966 on synthetic data, and a distortion classification accuracy of 96.97%. We further discuss the limitations and possible improvements over the proposed methods.

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Sara Esam Azmi, Chris Bartels, Zille Hussnain, J. A. P. Guelen, and Egor Bondarev "Detection and correction of image distortions induced by parasitic light sensitivity using deep learning methods", Proc. SPIE 12701, Fifteenth International Conference on Machine Vision (ICMV 2022), 127010V (7 June 2023); https://doi.org/10.1117/12.2679501

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KEYWORDS

Object detection

Data modeling

Deep learning

Education and training

Image restoration

Classification systems

Image classification

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