High Precision Techniques for Imaging through Turbulence

Abstract

Imaging through a turbulent medium, such as the atmosphere or the wavy surface of water, is highly desired in many scientific and military applications. This is a very challenging task due to the time-varying shifts and blurs captured in the images. This thesis deals with the geometrical restoration of such images captured as video sequences. These ordinarily undesirable geometrical distortions also act as information compressors and can be exploited to extract further bandwidth from the images to produce high-quality images from their lower resolution counterparts. The research investigations cover both the atmospheric as well as underwater imaging.

First, a simple and robust method is reviewed and improved upon to restore warped frames using motion vector fields (shiftmaps) obtained through a motion estimation technique. The centroid of the pixel shiftmaps is then calculated to generate individual restoration shiftmaps for each warped frame. The centroid shiftmap is updated iteratively to take the restored frames closer to their likely ground-truth. Furthermore, the image restoration method is made predictive by the use of a generalized regression neural network (GRNN), where the pixel shiftmaps amongst successive frames are used for training the network to determine the underlying warping functions, which in turn, are used to predict the upcoming warped frame. Moreover, the accurate motion analysis along with video stabilization method is utilized for reliable segmentation of video frames into stable and moving components and subsequently stabilizing frames, keeping real moving objects unaltered. Motivated by the successful application of GRNN in warp prediction, finally, a new and more efficient target tracking algorithm is proposed that works based on determining the centre and the area of moving objects, using those features for GRNN training, and employing the trained network to estimate the objects’ locations in the next frame.

Both the accuracy and the potential of the proposed algorithms have been investigated. The results presented are of both theoretical and practical interest and offer new efficient tools for substantial improvement of infrastructure of machine vision-based systems in general and of intelligent surveillance systems in particular.