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 stateof-the-art noise filtering methods.
Noise reduction is one of the most important topics of digital image processing and despite the fact that it has been studied for a long time it remains the subject of active research. In the following work, we present an extension of the Mean Shift technique, which is efficiently reducing the Gaussian noise, so that it is able to cope with the impulsive disturbances. Furthermore, the elaborated technique can be applied to enhance the images corrupted by a mixture of strong Gaussian and impulsive noise, severely decreasing the quality of color digital images. By means of our approach, which is based on a novel similarity measure between a pixel and a patch located in the center of the processing block, even heavily disturbed images can be effectively restored, which enables the success of further stages of the image processing pipeline. We evaluate the efficiency of the proposed method using a publicly available database of test color images and compare the restored images applying a set of standard quality metrics with the results delivered by state-of-the-art denoising methods. Additionally, we compare our method with the Medoid and Quick Shift techniques, accelerating the original Mean Shift algorithm, in terms of objective quality criteria and computational complexity. The results of the performed experiments indicate that the proposed technique is superior to the widely used denoising techniques and can be used as a robust extension of the Mean Shift procedure. In the paper, a particular emphasis is placed on the ability of the presented algorithm to preserve and enhance image edges. The performed experiments evaluated with the use of the Pratt’s index, quantitatively confirm the superiority of the proposed design over the Mean Shift and standard denoising methods. The preservation of edges and even their sharpening is a very important feature of our algorithm whereas the final goal is segmentation, playing a crucial role in various computer vision tasks. The proposed algorithm is intended for the mixed noise reduction in color images, but it can be also applied in multispectral imaging and clustering of multidimensional data. To enable the comparison of our method with the standard denoising techniques and to help applying it in other image processing fields, we made its code freely available.
In this paper, we address the problem of mixed Gaussian and impulsive noise reduction in color images. A robust filtering technique is proposed, which is utilizing a novel concept of pixels dissimilarity based on the reachability distance. The structure of the denoising method requires the estimation of the impulsiveness of each pixel in the processing block using the introduced local reachability concept. Furthermore, we determine the similarity of each pixel in the block to the central patch consisting of the processed pixel and its neighbors. Both measures are calculated as an average of modified reachability distances to the most similar pixels of the central patch and the final filtering output is a weighted average of all pixels belonging to the processing block. The proposed technique was compared with widely used filtering methods and the performed experiments proved its satisfying denoising properties. The introduced filtering design is insensitive to outliers and their clusters introduced by the impulsive noise process, preserves details and is able to efficiently suppress the Gaussian noise while enhancing the image edges. Additionally, we proposed a method which estimates the noise contamination intensity, so that the proposed filter is able to adaptively tune its parameters.
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