R. Lukac

Bio: R. Lukac is an academic researcher from Technical University of Košice. The author has contributed to research in topics: Median filter & Filter (signal processing). The author has an hindex of 9, co-authored 17 publications receiving 514 citations.

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.

LUM smoother with smooth control for noisy image sequences

Abstract: This paper focuses on adaptive structure of LUM (lower-upper-middle) smoothers for noisy image sequences. For the balance between noise suppression and signal-detail preservation, the LUM smoothers are widely used in smoothing applications. The amount of smoothing done by LUM smoothers is controlled by tuning parameter. However, the smoothing level is fixed for whole image. Thus, the excessive or insufficient smoothing can be performed. This problem is solved by a new method based on the adaptive controlled level of smoothing. A new method has excellent performance of the noise reduction in the environments corrupted by the impulse noise. In addition, minimal signal-detail and motion blurring can be observed. The performance of proposed method is evaluated through objective criteria and compared with traditional temporal, spatial, and spatiotemporal LUM smoothers.

Generalized adaptive vector sigma filters

Abstract: In this paper we provide a new filtering scheme for the detection and the removal of impulsive noise in digital color images. The proposed adaptive nonlinear vector filters take the advantages of the robust order-statistic theory, generalized directional distance filter and standard sigma filter concept. The principles of the design are explained in detail. Simulation studies indicate that the proposed method is computationally attractive and is able to achieve excellent balance between the image-detail preservation and the noise attenuation.

Weighted vector median optimization

Abstract: In this paper, a vector generalization of weighted median optimization approaches is provided. The proposed optimized weighted vector median filters utilize the relationship between standard median filter and vector median filter and also take the advantage of the generalized adaptive optimization algorithms initially developed for weighted median filters.

Multivariate Density Estimation

Abstract: Exploring and identifying structure is even more important for multivariate data than univariate data, given the difficulties in graphically presenting multivariate data and the comparative lack of parametric models to represent it. Unfortunately, such exploration is also inherently more difficult.