An Efficient Quad-tree based Impulse Detection and Rank-ordered Regularization for the Restoration of Highly Impulse corrupted Images

The paper proposes an efficient quad-tree based filtering algorithm for the restoration of impulse corrupted digital images. The quad-tree decomposition stage facilitates pixel classification in the impulse detection phase and minimizes miss-classification of signals as impulses by clearly distinguishing the high frequency image details from impulse corrupted pixels. The adaptive restoration phase identifies the most suitable signal restorer from among the true signals of a reliable neighborhood. Experimental results in terms of subjective assessment and objective metrics favor the proposed algorithm at all impulse noise levels over many top-ranking filters.