abstract = "surveillance systems has an important part as image
acquisition and filtering, segmentation, object
detection and tracking the object in that image. In
blind image de-convolution .most of the methods
requires that the PSF and the original image must be
irreducible. Blurring is a perturbation due to the
imaging system while noise is intrinsic to the
detection process. Therefore image de-convolution is
basically a post-processing of the detected images
aimed to reduce the disturbing effects of blurring and
noise. Image de-convolution implies the solution of a
linear equation ,but this problem turns out to be
ill-posed: the solution may not exist or may not be
unique. Moreover, even if a unique solution can be
found this solution is strongly perturbed by noise
propagation.In this papers we proposed a genetic
programming based blind-image de-convolution Blind
De-convolution algorithm can be used effectively when
of distortion is known. It restores image and Point
Spread Function (PSF) simultaneously. This algorithm
can be achieved based on Maximum Likelihood Estimation
(MLE).",
notes = "Shri Pannalal Research Institute of Technology.