Dual Histogram Based RVIN detector and Hybrid Gaussian Filter

Abstract

The objective of this paper is to develop an efficient noise detector and filter for random valued impulse noise (RVIN) in gray level images. A Dual Histogram based Noise Detector (DHND) is proposed and compared with other approaches using the classical measures like False Alarm (FA) and Miss Detection (MD). Also a novel filtering approach is proposed namely Hybrid Gaussian Filtering (HGF) which combines two filters such as Gaussian Impulse Kernel (GIK) and Gaussian Weighted Median (GWM). The proposed HGF approach is validated across state-of-art filters in terms of standard performance measures such as Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM) and Feature Similarity Index Measure (FSIM). The robustness of the filter is experimented for various standard gray scale images to evaluate its global significance. Experimental results reveal the pros and cons of other algorithms along with the effectiveness of the proposed filter under varying noise conditions.