Abstract

Digital images are often corrupted by impulse noise during image acquisition and/or transmission due to a number of non idealities encountered in image sensors and communication channels. In most image processing applications, it is of vital importance to remove the noise from the image data because the subsequent image processing tasks (such as segmentation and feature extraction, object recognition, etc.) are severely degraded by noise. A new operator for restoring digital images corrupted by impulse noise is presented. The proposed operator is a simple recursive switching median filter guided by neuro-fuzzy network functioning as an impulse detector. The proposed operator is a hybrid filter obtained by appropriately combining a median filter, an edge detector, and a neuro-fuzzy network. The internal parameters of the neuro-fuzzy network are adaptively optimized by training. The most distinctive feature of the proposed operator over most other operators is that it offers excellent line, edge, detail, and texture preservation performance while, at the same time, effectively removing noise from the input image. Extensive simulation experiments show that the proposed operator may be used for efficient restoration of digital images corrupted by impulse noise without distorting the useful information in the image.

Keywords

References

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