An efficient deep learning-based framework for image distortion correction

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