Fast adaptive switching technique of impulsive noise removal in color images

Abstract

In the paper, a family of switching filters designed for the impulsive noise removal in color images is analyzed. The framework of the proposed denoising techniques is based on the concept of cumulated distances between the processed pixel and its neighbors. To increase the filtering efficiency, a robust scheme, in which the sum of distances to only the most similar pixels of the neighborhood serves as a measure of impulsiveness, was elaborated. As this trimmed measure is dependent on the image local structure, an adaptive mechanism was also incorporated. Additionally, a very fast design, which enables image denoising in practical applications, is proposed and the choice of the filter output, which is used to replace the noisy pixels, is discussed. The described family of filters was evaluated on a large set of natural test images and compared with the state-of-the-art restoration methods. The analysis of the achieved results shows that the novel filters outperform the existing techniques in terms of both denoising accuracy and computational complexity. In this way, the proposed techniques can be recommended for the application in various image and video enhancement tasks.

Sparse Approach in Filtering of Color Images Corrupted by Mixture Noises

“…The experimental results were performed using a set of 100 color test images proposed by Malinski [25]. Mentioned set contains images with different texture and fine details structure that can guarantee robustness of investigating techniques.…”

Deep learning for impulsive noise removal in color digital images

“…Additionally, we used the Structural SIMilarity index (SSIM c ) designed for color images [55], because it has demonstrated better agreement with human observers in image quality assessment than traditional metrics. In this work, the following state-of-the-art filters were taken for comparison: Denoising Convolutional Neural Network trained on impulsive noise (DnCNN) [42], Fast Averaging TABLE IV IMPACT OF THE TYPE OF DATASET USED IN THE TRAINING AND ITS SIZE ON THE AVERAGE WACC OF THE Peer Group Filter (FAPGF) [28], Fast Adaptive Switching Trimmed Arithmetic Mean Filter (FASTAMF) [24], Fast Fuzzy Noise Reduction Filter (FFNRF) [21], Fuzzy Rank-Ordered Differences Filter (FRF) [56], Impulse Noise Reduction Filter (INRF) [57], Patch-based Approach for the Restoration of Images affected by Gaussian and Impulse noise (PARIGI) [58], Peer Group Filter (PGF) [27]. As our final method in evaluation, we used two switching filters.…”

“…An effective approach to retain uncorrupted pixels is based on the switching concept [24]. A general scheme of switching filter is presented in Fig.…”

Decision-Based Marginal Total Variation Diffusion for Impulsive Noise Removal in Color Images

“…Jin et al [9] employ the quaternion theory. Malinski and Smolka [10] use adaptive switching technique. Particularly, after the concept of a peer group is introduced, many schemes based on it have been presented; for example, Morillas et al [11] use the fuzzy peer groups, and Malinski and Smolka [12] utilize the fast averaging peer group.…”