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The effectiveness of restoration techniques mainly depends on the accuracy of the image modeling. One of the most popular degradation models is based on the assumption that the image blur can be modeled as a superposition with an impulse response H that may be space variant and its output is subject to an additive noise. Our research aimed at the use of statistical concepts and tools for developing a new class of image restoration algorithms. Several variants of a heuristic scatter matrix based algorithm (HSBA), the algorithm HBA that uses the Bhattacharyya coefficient for image restoration, the heuristic regression based algorithm for image restoration and new approaches of image restoration based on the innovation algorithm are reported. The LMS type algorithm AMVR is presented. A comparative study is performed and reported on the quality and efficiency of the presented noise removal algorithms.