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Many improvements on image enhancemen have been achieved by The Nonnegativity And Support
constraints Recursive Inverse Filtering (NASRIF) algorithm. The Deterministic constraints such as non
negativity, known finite support, and existence of blur invariant edges are given for the true image. NASRIF
algorithms iterative and simultaneously estimate the pixels of the true image and the Point Spread
Function (PSF) based on conjugate gradients method. NASRIF algorithm doesn’t assume parametric
models for either the image or the blur, so we update the parameters of conjugate gradient method and the
objective function for improving the minimization of the cost function and the time for execution. We
propose a different version of linear and nonlinear conjugate gradient methods to obtain the better results
of image restoration with high PSNR.
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