Geman&McClure Stochastic Estimation for a Robust Iterative Multiframe SRR with Geman&McClure-Tikhonov Regularization

Traditionally, Super Resolution Reconstruction (SRR) is the process by which additional information is incorporated to enhance a low resolution image thereby producing a high resolution image. This paper proposes the robust SRR algorithm for any noise model using the stochastic regularization technique by minimizing a cost function. The Geman&McClure norm is used for measuring the difference between the projected estimate of the high quality image and each low high quality image and for removing outliers in the data. Moreover, the Geman&McClure-Tikhonov and Tikhonov regularization is incorporated in the proposed SRR algorithm in order to remove artifacts from the final answer and to improve the rate of convergence. Finally, a number of experimental results are presented to demonstrate the performance of the proposed algorithm in comparison to several previously published methods based on L1 and L2 norm using a several noise models such as noiseless, AWGN, Poisson Noise, Salt & Pepper Noise and Speckle Noise.