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Progress in the restoration of image sequences degraded by atmospheric turbulence

机译:大气湍流退化图像序列的恢复进展

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In principle, a still image can be reconstructed from a turbulent video of a static scene using the classic 'sum and deblur' approach (with or without a preliminary registration step). However, the performance of such turbulence recovery algorithms has so far been limited. In the last decade, significant progress has been achieved in non-rigid registration and in image deblurring. We revisit the turbulence recovery problem, incorporating state of the art registration and deblurring algorithms as building blocks within the sum and deblur framework. Accurate pre-registration of the input video frames narrows the spatial support of the effective blur kernel affecting the sum of the turbulent video sequence. Powerful registration is therefore crucial for successful reconstruction. We employ a two-phase registration process, consisting of rigid registration followed by non-rigid refinement. For rigid registration, we adopt the recent algorithm of Lazaridis and Petrou (2006). Using real turbulent video data, we demonstrate excellent turbulence recovery.
机译:原则上,可以使用经典的“求和去模糊”方法(带有或不带有初步配准步骤)从静态场景的湍流视频中重建静止图像。然而,迄今为止,这种湍流恢复算法的性能受到限制。在过去的十年中,在非刚性配准和图像去模糊方面已经取得了重大进展。我们重新讨论了湍流恢复问题,将最先进的配准和去模糊算法作为总和和去模糊框架内的构建块。输入视频帧的准确预注册会缩小有效模糊内核的空间支持,从而影响湍流视频序列的总和。因此,强大的注册对于成功重建至关重要。我们采用两阶段配准过程,包括刚性配准和非刚性优化。对于刚性配准,我们采用了Lazaridis和Petrou(2006)的最新算法。使用真实的湍流视频数据,我们展示了出色的湍流恢复能力。

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