A novel hybrid decision-based filter and universal edge-based logical smoothing add-on to remove impulsive noise

Authors: RAJANBIR SINGH GHUMAAN, PRATEEK JEET SINGH SOHI, NIKHIL SHARMA, BHARAT GARG

Abstract: This paper presents a novel hybrid filter along with a universal extension to remove salt and pepper noise even at a very high noise density. The proposed filter initially specifies a threshold and then denoises the image using a combination of linear, nonlinear, and probabilistic techniques. Furthermore, to improve the quality, a universal add-on is presented which uses edge detection and smoothening techniques to brush out fine details from the restored image. To evaluate the efficacy, the proposed and existing filtering techniques are implemented in MATLAB and simulated with benchmark images. The simulation results show that the proposed filter is able to restore image details even at the extremely high noise density of 99%. Moreover, the proposed filter provides admirable results on natural as well as medical images from very low to very high noise density. Finally, it is observed that, on average, the proposed filter improves the PSNR by 11% over the state-of-the-art technique.

Keywords: Salt and pepper noise, impulse noise, median filters, mean filter, image processing

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