Deep-learning-based adaptive camera calibration for various defocusing degrees

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. (C) 2021 Optical Society of America.