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This article proposes a novel method for restoring images corrupted with clusters of impulse noise. It is a durable task to detect and restore clusters of impulse noise because the cluster pixels can meet many of the well-known thresholds. In the proposed technique, a hard decision threshold is proposed based on the dissimilarities among the cluster pixels and the original pixels in the noisy image. The analysis revealed that the dissimilarity values of the cluster pixels are significantly different from those of the original pixels. Results achieved by the proposed algorithm are superior to other methods. The given method effectively suppresses the noisy pixels, preserving the fine details, having low-computational complexity, and maintaining high level of visual quality.

Localizing and restoring clusters of impulse noise based on the dissimilarity among the image pixels