In order to automatically enhance and restore images, especially those taken from underwater environments where scattering and absorption by the medium strongly influence the imaging results even within short distances, it is critical to have access to an objective measure of the quality of images obtained. This contribution presents an approach to measure the sharpness of an image based on the weighted gray-scale-angle (GSA) of detected edges. Images are first decomposed by a wavelet transform to remove random and part medium noises, to augment chances of true edge detection. Sharpness of each edge is then determined by regression to determine the slope between gray-scale values of edge pixels versus locations, which is the tangent of an angle based on grayscale. The overall sharpness of the image is the average of each measured GSAs, weighted by the ratio of the power of the first level decomposition details, to the total power of the image. Adaptive determination of edge widths is facilitated by values associated with image noise variances. To further remove the noise contamination, edge widths less than corresponding noise variances or regression requirement are discarded. Without losing generality while easily expandable, only horizontal edge widths are used in this study. Standard test images as well as those taken from field are used to be compared subjectively. Initial restoration results from field measured underwater images based on this approach and weakness of the metric are also presented and discussed.
['Weilin Hou', 'Alan Weidemann']
Objectively assessing underwater image quality for the purpose of automated restoration