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Ability to image underwater is highly desired for scientific and military applications, including optical communications, submarine awareness, diver visibility, and mine detection. Underwater imaging is severely impaired by scattering and optical turbulence associated with refraction index fluctuations. This work introduces novel approach to restoration of degraded underwater imagery based on multi-frame correction technique developed for atmospheric distortions. The method represents synthesis of "lucky-region" fusion and optical flow based image warping. Developed multi-frame image restoration algorithm is applied to sets of images collected in laboratory under controlled conditions as well as field test data. Reliance of image restoration on sophistication of the optical flow algorithm is shown. Variable degrees of image degradation mitigation which manifest themselves as high spatial frequency content recovery are demonstrated depending on imaging conditions and ratio of typical image spatial frequency scale to typical degradation spatial frequency scale.

It is a well-known fact that the major degradation source on EO imaging underwater is from scattering by the medium itself and the constituents within, namely particles of various origins and sizes. Recent research indicates that under certain conditions, such degradations could be caused mainly by the variations of index of refraction associated with temperature and salinity micro-structures in the ocean. These would inherently affect the optical signal transmission underwater, which is of vital interest to both civilian and military applications, as they could include diver visibility, search and rescue, mine detection and identification, and optical communication. The impacts from the optical turbulence are yet fully understood, in part due to the challenges associated with parameterization of individual factors. This study presents the initial attempts in quantifying the level of EO image degradation due to optical turbulence in natural waters, in terms of modulation transfer functions, and enhancements using the lucky patch approaches derived from optical flow techniques. Image data collected from natural environments during SOTEX (Skaneateles Optical Turbulence Exercise, July 22-31, 2010) using the Image Measurement Assembly for Subsurface Turbulence (IMAST) are presented. Optical properties of the water column were measured using WETLab's ac-9 and LISST (Laser In Situ Scattering and Transmissiometry), in coordination with temperature, conductivity and depth. Turbulence conditions were measured by two different approaches, namely a 3D Doppler based velocimeter with Conductivity/Temperature combo, and a shear based Vertical Microstructure Profiler.

Multi-frame super-resolution of images containing complex motion fields remains an elusive target requiring precise estimation of such motion between the frames. Although accuracy of optical flow algorithms has been increasing steadily it is not yet sufficient to provide confident subpixel resolution enhancement during super-resolution reconstruction. One way to approach the problem is to augment optical flow estimation input into super-resolution procedure by reliable confidence measure. Numerous optical flow confidence or conversely uncertainty measures have been developed recently. However, their performance has been studied and evaluated with respect to error metric involving optical flow vectors, namely angular and end point errors averaged over the entire image. Super-resolution processing requires consideration of interpolation error i.e. the difference between the reference image and the motion compensated image. This work presents comparative evaluation of optical flow confidence measures and makes conclusions of their usefulness to applications utilizing image compensation.

Robust fusion of data from disparate sensor modalities can provide improved target detection performance over those attainable with the individual sensors. In particular, detection of low-radiance manmade objects or objects under shadow obscuration in hyperspectral imagery (HSI) with acceptable false alarm rates has proven especially challenging. We have developed a fusion algorithm for the enhanced detection of difficult targets when the HSI data is simultaneously collected with LADAR data. Initial detections are obtained by applying a sub-space RX (SSRX) algorithm to the HSI data. In parallel, LADAR-derived digital elevation map (DEM) is segmented and coordinates of objects within a specific elevation range and size are returned to the HSI processor for their spectral signature extraction. Each extracted signature that has not been already detected by SSRX is used in secondary HSI detection employing the adaptive cosine estimator (ACE) algorithm. We show that spatial distribution of ACE score allows for confident discrimination between background elevations and manmade objects. Key to cross-characterization of the data is the accurate co-alignment of the image data. We have also developed an algorithm for automatic co-registration of ladar and HSI imagery, based on the maximization of mutual information, which can provide accurate, sub-pixel registration even if the case when the imaging geometries for the two sensors differ. Details of both algorithms will be presented and results from application to field data will be discussed.