An Adaptive And Statistical Efficiency Myriad Filter For A Recursive Image Reconstruction Using a Multi-Frame SRR Algorithm With A Stochastic Regularization For Video Sequences

Abstract

In real applied implementations, a collection of classical linear filtering theories, for example, a Median filter and a Mean filter, can be only applied to the Gaussian noise environments due to the fact that these linear filters usually gives the poor performance under the presence of non-Gaussian noise environments. Because of the motion estimation blunder and observation process error, which are usually caused from real electronic noise, non-accurate optical devices or mathematical simplification models of observed process systems, the SRR (Super Resolution Reconstruction) algorithms using classical linear filters (a Median filter and a Mean filter) should possibly demote the quality of a reconstructed image rather than improve its quality. Under non-Gaussian environments, a class of flexible filters with high statistical efficiency, so called Myriad filter, has been proposed and its solid theoretical principle was analyzed for indicating that the Myriad filter is usually more powerful than a class of linear filters, especially for non-Gaussian environments. This paper proposes an adaptive and statistical efficiency myriad filter for a recursive image reconstruction for applied implementing on real video sequences, which are contaminated by both Gaussian and non-Gaussian noise environments. Thus, the Myriad filter, which is implemented for getting rid of noise in an expected image and for valuing the contrast between the back-propagated expecting of the reconstructed high resolution image and a group of low resolution images, is involved in this stochastic regularization SRR framework for the elimination proposing of Gaussian and non-Gaussian noise. Because of an ill-pose condition of the SRR algorithm, Tikhonov regularization methodology is mathematically required for getting rid of deformation from the reconstructed high resolution image and reforming the calculated time of its convergence. Under a lot of noisy corrupted environments (Noiseless, AWGN, Impulsive Noise, Poisson Noise and Speckle Noise at unequal noise power), the performance from the both PSNR and virtual quality prospect of the proposed SRR algorithm using Myriad filter, which is compared with SRR algorithms using classical linear filters (a Median filter and a Mean filter) are depicted and the proposed SRR algorithm gives the superior visually quality and, thus, superior PSNR than the SRR algorithms using classical linear filters.