Digital imaging sensor technology has continued to outpace development in optical technology in modern imaging systems. The resulting quality loss attributable to lateral chromatic aberration is becoming increasingly significant as sensor resolution increases; other classes of aberration are less significant with classical image enhancement (e.g. sharpening), whereas lateral chromatic aberration becomes more significant. The goals of higher-performance and lighter lens systems drive a recent need to find new ways to overcome resulting image quality limitations. This work demonstrates the robust and automatic minimisation of lateral chromatic aberration, recovering the loss of image quality using both artificial and real-world images. A series of test images are used to validate the functioning of the algorithm, and changes across a series of real-world images are used to evaluate the performance of the approach.
翻译:数字成像传感器技术继续超过现代成像系统中光学技术的发展速度,随着传感器分辨率的增加,由于横向色相畸变造成的质量损失越来越严重;随着古典图像的增强,其他类型的畸变则不太重要(例如磨剪),而横向色相畸变则变得更加重要。高性能和较轻镜头系统的目标促使最近需要找到新的方法,克服由此产生的图像质量限制。这项工作表明横向色相畸变的稳健和自动最小化,利用人造图像和真实世界图像恢复图像质量的丧失。一系列测试图像被用来验证算法的功能,并使用一系列真实世界图像的变化来评价该方法的性能。