FixPix: Fixing Bad Pixels using Deep Learning
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866914885752848384 |
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| author | Sarkar, Sreetama Ye, Xinan Datta, Gourav Beerel, Peter A. |
| author_facet | Sarkar, Sreetama Ye, Xinan Datta, Gourav Beerel, Peter A. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_11637 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | FixPix: Fixing Bad Pixels using Deep Learning Sarkar, Sreetama Ye, Xinan Datta, Gourav Beerel, Peter A. Image and Video Processing 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. |
| title | FixPix: Fixing Bad Pixels using Deep Learning |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2310.11637 |