Robust local similarity filter for the reduction of mixed Gaussian and impulsive noise in color digital images

Bogdan Smolka,Damian Kusnik

doi:10.1007/s11760-015-0830-0

Bogdan Smolka, Damian Kusnik

Open Access

https://doi.org/10.1007/s11760-015-0830-0

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Abstract

In this paper, a novel technique designed for the suppression of mixed Gaussian and impulsive noise in color images is proposed. The new denoising scheme is based on a weighted averaging of pixels contained in a filtering block. The main novelty of the proposed solution lies in the new definition of the similarity between the samples of the processing block and a small window centered at the block’s central pixel. Instead of direct comparison of pixels, a measure based on the similarity between a given pixel and the samples from the neighborhood of the central pixel is utilized. This measure is defined as the sum of distances in a given color space, between a pixel of the block and a certain number of most similar samples from the filtering window. The main advantage of the proposed scheme is that the new similarity measure is not influenced by the outliers injected into the image by the impulsive noise and the averaging process ensures the effectiveness of the new filter in the reduction of Gaussian noise. The experimental results prove that the novel filtering design is capable of suppressing mixed noise of high intensity and is competitive with respect to the state-of-the-art noise filtering methods.

Highlights

Noise reduction is one of the most frequently performed image processing operations, as the enhancement of degradedFaculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland images or video streams is crucial for the success of the consecutive image processing steps.Very often color images are corrupted by various types of noise, introduced by malfunctioning or damaged sensors in the image formation process, poor lighting conditions and aging of the storage material, electronic instabilities of the signal, transmission errors in noisy channels and atmospheric as well as electromagnetic interferences
The suppression of this kind of noise is a challenging task, as filters designed to cope with the Gaussian noise are not able to remove the impulses and the techniques designed for the impulsive noise removal are ineffective in the elimination of the noise modeled by the Gaussian distribution [6,22,23, 37, 49, 50]
The popular, highly effective techniques like Non-Local Means (NLM) [3,4], Block-Matching and 3D Filtering (BM3D) [9] or Bilateral Filter (BF) [52] are unable to suppress the impulses and the final filtering result is of unacceptable quality

Summary

Introduction

Noise reduction is one of the most frequently performed image processing operations, as the enhancement of degraded. Very often the mixed noise is being suppressed by applying first a filter suitable for the removal of impulsive and the one intended for the reduction of Gaussian noise Such a scheme is not effective, as the consecutive application of two filters leads to significant image distortions and substantial increase in the computational burden [27]. Another approach to the problem of mixed noise reduction is based on the switching filtering [10,18,27], which detects the impulses and removes them using a suitable technique replacing the remaining samples with a filter designed for the Gaussian noise. K=1 where K denotes the kernel function (e.g., Gaussian) and d j(k) is the kth smallest Euclidean distance in the RGB color space between x j and the pixels of W

Experiments

Conclusions