Statistical analysis of Image restoration using Geometric Transform approach

While digital imaging systems have been widely used for many applications including consumer photography, microscopy, aerial photography, astronomical imaging, etc., their output images/videos often suffer from spatially varying blur caused by lens, transmission medium, post processing algorithms, and camera/object motion. Measuring the amount of blur globally and locally is an important issue. It can help us in removing the spatially varying blur, and enhancing the visual quality of the imaging system outputs. In this paper, we study the blur measurement problem for different scenarios. We have applied the Geometric Transformation algorithm for restoration of the blurred image. First we apply it for that image where there is only spatial variation in terms of coordinate geometry by keeping the neighborhood pixels orientation constant. Then we apply the algorithm for the case where there is geometric variation spatial as well as local. In both cases we estimated the PSNR & MSE value. Geometric Transformation methods provide us an easiest way to restore the match because here only matched features points are involved in the process, Whereas in local probability estimation we have to concentrate over all pixels and then cause so the computation become complex and hence it enhances the cost of the system.