Rastislav Lukac

Bio: Rastislav Lukac is an academic researcher. The author has contributed to research in topics: Filter (signal processing) & Smoothing. The author has an hindex of 1, co-authored 1 publications receiving 123 citations.

Adaptive Color Image Filtering Based on Center-Weighted Vector Directional Filters

Abstract: This paper presents a new filtering approach for impulsive noise removal in color images. Incorporating the nonnegative integer weight corresponding to the central sample into the structure of the basic vector directional filter (BVDF), the proposed framework constitutes a class of center-weighted vector directional filters (CWVDF). It can be easily observed that the CWVDF filters are computationally efficient and extend design flexibility of the standard BVDF scheme. By varying the center weight, the proposed CWVDF framework can provide the smoothing characteristics ranging from an identity operation to that of the BVDF. Therefore, design characteristics relate to the CWVDF, which removes impulses and outliers from the image while simultaneously preserving the structural information. To adaptively determine the optimal value of the center weight, two adaptive approaches based on the angular thresholds are provided. Both techniques achieve excellent results in terms of the commonly used objective image quality criteria and significantly outperform standard multichannel filtering algorithms.

Vector filtering for color imaging

Abstract: Vector processing operations use essential spectral and spatial information to remove noise and localize microarray spots. The proposed fully automated vector technique can be easily implemented in either hardware or software; and incorporated in any existing microarray image analysis and gene expression tool.

Fast detection and impulsive noise removal in color images

Abstract: In this paper, a novel approach to the impulsive noise removal in color images is presented. The proposed technique employs the switching scheme based on the impulse detection mechanism using the so-called peer group concept. Compared to the vector median filter and other commonly used multichannel filters, the proposed technique consistently yields very good results in suppressing both the random and fixed-valued impulsive noise. The main advantage of the proposed noise detection framework is its enormous computational speed, which enables efficient filtering of color images in real-time applications.

A fast impulsive noise color image filter using fuzzy metrics

Abstract: In this paper, the problem of impulsive noise reduction in multichannel images is addressed. A new filter is proposed on the basis of a recently introduced family of computationally attractive filters with a good detail-preserving ability (FSVF). FSVF is based on privileging the central pixel in each filtering window in order to replace it only when it is really noisy and preserve the original undistorted image structures. The new filter is based on a novel fuzzy metric and it is created by combining the mentioned scheme and the fuzzy metric. The use of the fuzzy metric makes the filter computationally simpler and it allows to adjust the privilege of the central pixel giving the filter an adaptive nature. Moreover, it is shown that the new filter outperforms the classical-order statistics filtering techniques and its performance is similar to FSVF, outperforming it in some cases.

Fuzzy Peer Groups for Reducing Mixed Gaussian-Impulse Noise From Color Images

Abstract: The peer group of an image pixel is a pixel similarity-based concept which has been successfully used to devise image denoising methods. However, since it is difficult to define the pixel similarity in a crisp way, we propose to represent this similarity in fuzzy terms. In this paper, we introduce the fuzzy peer group concept, which extends the peer group concept in the fuzzy setting. A fuzzy peer group will be defined as a fuzzy set that takes a peer group as support set and where the membership degree of each peer group member will be given by its fuzzy similarity with respect to the pixel under processing. The fuzzy peer group of each image pixel will be determined by means of a novel fuzzy logic-based procedure. We use the fuzzy peer group concept to design a two-step color image filter cascading a fuzzy rule-based switching impulse noise filter by a fuzzy average filtering over the fuzzy peer group. Both steps use the same fuzzy peer group, which leads to computational savings. The proposed filter is able to efficiently suppress both Gaussian noise and impulse noise, as well as mixed Gaussian-impulse noise. Experimental results are provided to show that the proposed filter achieves a promising performance.