Water turbidity is a frequent impediment for achieving satisfactory imaging clarity in underwater video and inhibits the extraction of information concerning the condition of submerged structures. Ports, rivers, lakes and inland waterways are notoriously difficult spots for camera inspections due to poor visibility. This problem motivated us to study methods to extract a cleaner image /video from the one acquired in an almost real-time setting (delay of the order of 6–7 secs). This type of problem arises in image post-processing as an illumination neutralization problem and, it can also be viewed as a blind deconvolution problem. We present a method which enables the derivation of a cleaner image from a poor visibility original by means of a combination of linear and non-linear deterministic mathematical transformations for illumination neutralization implementable in almost real-time on GPUs. Real time visibility improvement for marine and water environments is a suite of algorithms aiming to restore the visibility of images and videos acquired under the surface of every water body utilizing our illumination neutralization method. We are currently transitioning from an academic algorithmic suite to a product we will call αλσvision (ALSvsion) from the Homerian Greek word αλσ which means sea. Upon completion of the project, the GUI will enable the view of the original camera feed and of the visibility improved video side-by-side. This software is not intended to replace the original video feed, but to offer guidance for interpreting it. αλσvision can be used for a variety of visual inspections in marine and offshore industries. The method we present also works for visibility restoration in still images or videos acquired in poor atmospheric conditions such as fog, haze, smoke or with insufficient illumination. One of our main contributions is the development of a mathematical theory which enables the derivation of results showing that lines and textures significant for the identification of structures and of structural problems are made visible as they appear in an image or video acquired in good imaging conditions.

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