Zhang Jing, Luo Bin, Xiang Zhuolong, Zhang Qican, Wang Yajun, Su Xin, Liu Jun, Li Lu, Wang Wei
Opt Lett. 2021 Nov 15;46(22):5537-5540. doi: 10.1364/OL.443337.
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.
相机校准往往会受到低质量目标图像采集的影响，这会导致提取的特征不准确或不充分，从而导致参数估计不精确甚至失败。为了解决这个问题，本文提出了一种基于深度学习的新型自适应校准方法，该方法对散焦和噪声具有鲁棒性，可以显著提高图像质量并有效改善校准结果。我们的工作提供了一种方便的多质量目标数据集生成策略，并引入了一个多尺度深度学习框架，该框架成功地从退化的图像中恢复出清晰的目标图像。该方法无需捕获额外的图案或使用特殊的校准目标，允许基于采集到的低质量图像进行更可靠的校准。在本研究中，可以轻松建立一个初始训练数据集，该数据集仅包含由智能手机拍摄的68张图像。基于增强后的数据集，在校准实验中验证了该方法在另一台相机上的优越性能和灵活的可迁移能力。
