Complex impulse noise removal from color images based on super pixel segmentation

An effective method for removal of mixed single-point and granular impulse noise.A new method for classification of noisy pixels based on super pixel segmentation.A selected recursive vector median filter with adaptive window sizes.An efficient algorithm for computing the circularity of a region. Impulse noise sometimes appears as blob or granular shapes in images, which are irregularly shaped with typically several pixels wide in different directions. Most existing methods are developed to remove only single-point impulse noise and usually perform poor when applied to blob noise removal. This paper presents a new method to suppress such complex blob noise with varying sizes and irregular shapes in color images. First, a noisy image is segmented into super pixels by mean shift filtering followed by a clustering operation based on quaternion color distance. Then, by analyzing the characteristics of super pixels, image pixels are classified into noise-free, blob-noisy, and single-point impulse ones. Finally, a selected recursive vector median filter with adaptive window sizes is employed on the noisy pixels detected. The experimental results exhibit the validity of the proposed solution by showing excellent denoising effect and performance, compared to other color image denoising methods.

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