Hussain Abo Surrah
One well-studied image processing task is the removal of impulse noise from images. Images are often corrupted by impulse noise due to errors generated in noisy sensors, communication channels, or during storage. It is important to eliminate noise in the images before some subsequent processing, such as edge detection, image segmentation and object recognition. For this purpose, many approaches have been proposed. In the past two decades, median-based filters have attracted much attention because of their simplicity and their capability of preserving image edges. Nevertheless, because the typical median filters are implemented uniformly across the image, they tend to modify both noise and good pixels. To avoid the distortion of good pixels, the switching approach is introduced by some published works, In this case the impulse detection algorithms are employed before filtering and the detection results are used to control whether a pixel should be modified or not. This approach has been proved to be more effective than uniformly applied methods when the noise pixels are sparsely distributed in the image. However, when the images are very highly corrupted, a large number of impulse pixels may connect into noise blotches. In such cases, many impulses are difficult to be detected, thus can’t be eliminated. In addition, the error will propagate around their neighborhood regions. In this paper, we propose a technique based on impulse noise detection by means of a self-organizing neural network and a class of the switching filters that can remove impulse noise effectively while preserving details. Also, we add a histogram equalizer filter at the output of our proposed system in order to enhance the final output images. Experimental results demonstrate that the performance of the proposed technique is superior to that of the traditional median filter family for impulse noise removal in image applications.
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