An efficient deep learning-based framework for image distortion correction

Sicheng Li, Yuhui Chu, Yunpeng Zhao, Pengpeng Zhao
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Abstract

Geometric distortions in digital images, caused by factors such as lens defects and changes in camera angles, substantially influence the fidelity of the image by altering pixel positions and shapes. Current geometric distortion correction methods, focusing on specific types of distortions and relying on high computational resources, face limitations in universality and practicality across diverse real-world applications. We propose here a two-stage distortion correction method that integrates deep learning with traditional image registration algorithms for correcting multiple types of geometric distortion. Compared to state-of-the-art correction methods, our proposed method demonstrates flexibility, capable of addressing a wide range of geometric distortions and achieves superior correction results with fewer parameters. In addition, tests performed on synthetic datasets show an improvement of 10.39% for PSNR, 30.42% for SSIM, and 85% for processing speed, compared to the best performing methods to our knowledge. Finally, experiments with handheld medical endoscopic scanners confirm the applicability and robustness of our method in real-world scenarios. Our method offers a versatile and efficient solution for geometric distortion correction, suitable for various applications, including medical imaging and resource-limited embedded systems. Code is available at https://github.com/MaybeRichard/EffiGeoNet

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基于深度学习的高效图像失真校正框架
数字图像中的几何畸变是由镜头缺陷和相机角度变化等因素造成的,它通过改变像素的位置和形状而严重影响图像的保真度。目前的几何畸变校正方法主要针对特定类型的畸变,依赖于较高的计算资源,在现实世界的各种应用中面临普遍性和实用性的限制。我们在此提出一种两阶段畸变校正方法,该方法将深度学习与传统图像配准算法相结合,可校正多种类型的几何畸变。与最先进的校正方法相比,我们提出的方法具有灵活性,能够解决各种几何畸变问题,并以较少的参数实现卓越的校正效果。此外,在合成数据集上进行的测试表明,与我们所知的性能最好的方法相比,PSNR 提高了 10.39%,SSIM 提高了 30.42%,处理速度提高了 85%。最后,使用手持医疗内窥镜扫描仪进行的实验证实了我们的方法在现实世界中的适用性和鲁棒性。我们的方法为几何畸变校正提供了一个多功能、高效的解决方案,适用于各种应用,包括医疗成像和资源有限的嵌入式系统。代码见 https://github.com/MaybeRichard/EffiGeoNet
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