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 the use of statistical concepts and tools for developing a new class of image restoration algorithms. Several variants of a heuristic scatter matrices based algorithm (HSBA), the algorithm HBA that uses the Bhattacharyya coefficient for image restoration, the heuristic regression based algorithm for image restoration and a new approaches of image restoration based on the innovations algorithm are reported. The LMS type algorithm AMVR is presented in the final section of the paper. A comparative study is performed and reported on the quality and efficiency of the presented noise removal algorithms.

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Abstract. The work proposes a new algorithm for the estimation of the ICA model, an algorithm based on secant method and successive approximations. The first sections briefly present the standard FastICA algorithm based on the Newton method and a new version of the FastICA algorithm. The proposed algorithm to estimate the independent components combines the secant iterations with successive approximations technique. The final section presents the results of a comparative analysis experimentally derived conclusions concerning the performance of the proposed method. The tests were performed of several samples of signal files.