Abstract

Camera calibration tends to suffer from the low-quality target image acquisition, which would yield inaccurate or inadequate extracted features, resulting in imprecise or even failed parameter estimation. To address this problem, this Letter proposes a novel deep-learning-based adaptive calibration method robust to defocus and noise, which could significantly enhance the image quality and effectively improve the calibration result. Our work provides a convenient multi-quality target dataset generation strategy and introduces a multi-scale deep learning framework that successfully recovers a sharp target image from a deteriorated one. Free from capturing additional patterns or using special calibration targets, the proposed method allows for a more reliable calibration based on the poor-quality acquired images. In this study, an initial training dataset can be easily established containing only 68 images captured by a smartphone. Based on the augmented dataset, the superior performance and flexible transferable ability of the proposed method are validated on another camera in the calibration experiments.

© 2021 Optical Society of America

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