Impulse noise reduction in color images based on mean shift and channel suppression

Many existing methods effectively remove impulse noise from images, but they usually perform well only in a certain noise level (i.e., either low or high contamination). An impulse noise detection technique that is based on mean shift filtering and an effective vector filtering method that is based on channel suppression processing are proposed. The proposed denoising method excellently suppresses impulse noise in color images and performs well in both low and medium noise contamination cases. First, the noise detection method is performed to divide image pixels into noisy and possibly noise-free ones. Then, for the noisy pixels detected, according to the contamination levels, different denoising strategies are employed. For slightly corrupted images, the proposed channel-suppressed vector filter is performed, whereas for medially and highly contaminated images, a total variation denoising technique is applied, followed by a further impulse noise detection and channel-suppressed filtering. Finally, for the possible noise-free pixels, fine noise detection and channel-suppressed filtering are performed. Extensive simulation results exhibit the validity of the proposed solution by showing clear performance improvements over other widely used color image filtering methods.