Automated Underwater Image Restorations and Retrieval of Related Optical

dThe presented effort is aimed at establishing a the blurring caused by strong scattering due to water and its framework in order to restore underwater imagery to the best constituents which includes various sized particles. To properly possible level, working with both simulated and field measured address this issue, knowledge of in-water optical properties and data. Under this framework, the traditional image restoration their relationship to the image formation can be exploited in approach is extended by incorporating underwater optical order to restore the imagery to the best possible level. This in properties into the system response function, specifically the turn provides much needed environmental information via point spread function (PSF) in spatial domain and modulation through-the-sensor techniques and greatly enhance current transfer function (MTF) in frequency domain. Due to the operational capabilities. intensity variations involved in underwater sensing, denoising is carefully carried out by wavelet decompositions. This is necessary to explore different effects of restoration constrains, Ii. FRAMEWORK COMPONENTS and especially their response to underwater environment where the effects of scattering can be easily treated as either signal or A. Image Restoration noise. The images are then restored using measured or modeled Generally speaking, a 2-dimentional image of an object is PSFs. An objective image quality metric, tuned with basically the combination of original signal, f(x,y), convolved environmental optical properties, is designed to gauge the effectiveness of the restoration, and serves to check the by the imaging system response of a point source, the point optimization approach. This metric utilizes previous wavelet spread function or PSF h(x,y), integrated over sensor space Z: decompositions to constrain the sharpness metric based on grayscale slopes at the edge, weighted by the ratio of the power of g(x,y) = ff(xi,yi)h(x-xi,y yi)dxidyj ,(!) high frequency components of the image to the total power of the image. Modeled PSFs, based on Wels' small angle approximations, are compared to those derived from Monte The system response includes returns from both the Carlo simulation using measured scattering properties. Initial imaging system itself, as well as the effects of the medium. results are presented, including estimation of water optical properties from the imagery-derived MTFs, and optimization Mathematically, it is easier to manipulate the above outputs applying automated restoration framework. relationship in the frequency domain as the convolution operator becomes a simple multiplication. Applying a Fourier Keywordsocean optics; scattering; image restoration; transform, the above relationship becomes moduiation transfer function; point spreadfunction; NIRDD G(u, v) = F(u, v)H(u, v), (2)