High density impulse noise removal based on linear mean-median filter

This paper presents Linear Mean-Median (LMM) filter that used to reduce impulse noise. LMM filter is a combination between Mean and Median filter. Wherein, linear value is acquired from the linearity between mean and median value. Mean and Median filter are only applied for free-noise pixel on the 3×3 windows that has been sorted from the smallest to the largest value. The mean value is obtained from the average value of all free-noise pixels without including the median pixel position. Meanwhile, median pixel is the middle position of the pixel that has been sorted. LMM uses nine sample pixels to determine a pixel for replacement a corrupted pixel. Our filter also provides the impulse noise prediction systems that serve as a facilitator to give information about noise content. If the noise is greater than 30%, the performance of LMM filter needs to be improved by an adaptive rank order mean filters. The filtering results have shown satisfactory results in terms of the quality result and the computation time process. A good image quality can be evidenced by PSNR (Peak Signal to Noise Ratio). Our methods always have higher PSNR value than the comparison methods. In addition, the speed computation time of our method is faster than the comparison method.