The presented effort is aimed at establishing a framework in order to restore underwater imagery to the best possible level, working with both simulated and field measured data. Under this framework, the traditional image restoration approach is extended by incorporating underwater optical properties into the system response function, specifically the point spread function (PSF) in spatial domain and modulation transfer function (MTF) in frequency domain. Due to the intensity variations involved in underwater sensing, denoising is carefully carried out by wavelet decompositions. This is necessary to explore different effects of restoration constrains, and especially their response to underwater environment where the effects of scattering can be easily treated as either signal or noise. The images are then restored using measured or modeled PSFs. An objective image quality metric, tuned with environmental optical properties, is designed to gauge the effectiveness of the restoration, and serves to check the optimization approach. This metric utilizes previous wavelet decompositions to constrain the sharpness metric based on grayscale slopes at the edge, weighted by the ratio of the power of high frequency components of the image to the total power of the image. Modeled PSFs, based on Wells' small angle approximations, are compared to those derived from Monte Carlo simulation using measured scattering properties. Initial results are presented, including estimation of water optical properties from the imagery-derived MTFs, and optimization outputs applying automated restoration framework.
