Showing papers by "Bogdan Smolka published in 2005"

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 Statistically-Switched Adaptive Vector Median Filter

Abstract: This paper presents a new cost-effective, adaptive multichannel filter taking advantage of switching schemes, robust order-statistic theory and approximation of the multivariate dispersion. Introducing the statistical control of the switching between the vector median and the identity operation, the developed filter enhances the detail-preserving capability of the standard vector median filter. The analysis and experimental results reported in this paper indicate that the proposed method is capable of detecting and removing impulsive noise in multichannel images. At the same time, the method is computationally efficient and provides excellent balance between the noise attenuation and signal-detail preservation. Excellent performance of the proposed method is tested using standard test color images as well as real images related to emerging virtual restoration of artworks.

Ultrafast technique of impulsive noise removal with application to microarray image denoising

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, the proposed technique consistently yields better results in suppressing both the random-valued and fixed-valued impulsive noise. The main advantage of the proposed noise detection framework is its enormous computational speed, which enables efficient filtering of large images in real-time applications. The proposed filtering scheme has been successfully applied to the denoising of the cDNA microarray images. Experimental results proved that the new filter is capable of removing efficiently the impulses present in multichannel images, while preserving their textural features.

Soft-Switching adaptive technique of impulsive noise removal in color images

Abstract: In this paper a novel class of filters designed for the removal of impulsive noise in color images is presented. The proposed filter family is based on the kernel function which regulates the noise suppression properties of the proposed filtering scheme. The comparison of the new filtering method with standard techniques used for impulsive noise removal indicates superior noise removal capabilities and excellent structure preserving properties.

A data-adaptive approach to cDNA microarray image enhancement

Abstract: A data-adaptive approach for cDNA microarray image enhancement is presented. Through the weighting coefficients adaptively determined from local microarray image statistics, the proposed technique tunes the overall filter's detail-preserving and noise-attenuating characteristics and uses both the spatial and spectral correlation of the cDNA image during processing. Noise removal is performed by tuning a membership function which utilizes the aggregated absolute differences between the cDNA microarray inputs localized within a processing window sliding over the image.

Adaptive soft-switching filter for impulsive noise suppression in color images

Abstract: In this paper a new class of filters designed for the removal of impulsive noise in color images is presented. The proposed filter class is based on the nonparametric estimation of the density probability function of pixels in a sliding filtering window. The comparison of the new filtering method with the standard techniques used for impulsive noise removal, indicates good noise removal capabilities and excellent structure preserving properties.

On the adaptive impulsive noise removal in color images

Abstract: In this paper a novel adaptive filtering scheme for impulsive noise removal in color images is presented. The noise detection algorithm is based on the concept of aggregated distances assigned to the pixels belonging to the filtering window. The value of the difference between the accumulated distance assigned to the central sample and to the pixel with the lowest rank, serves as an indicator of the presence of impulses injected into the image by the noise process. The output of the proposed filter is a weighted mean of the central pixel of the filtering window and the vector median of its samples. The obtained results show that the proposed filter outperforms existing impulse noise removal techniques for low noise contamination and can be used in various applications in which the detail preserving reduction of impulses play an important role.

Fast Detection and Impulsive Noise Attenuation 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, the proposed technique consistently yields better results in suppressing both the random-valued and fixed-valued impulsive noise. The main advantage of the proposed noise detection framework is its enormous computational speed, which enables efficient filtering of large images in real-time applications.

Application of the adaptive noise removal technique to the enhancement of cDNA microarray images

Abstract: In this paper a novel class of filters designed for the removal of impulsive noise in colour images is presented. The proposed filter family is based on the kernel function which controls the noise suppression properties of the new filtering scheme. The comparison of the new filtering method with the standard techniques used for impulsive noise removal indicates its superior noise removal capabilities and excellent structure preserving properties. The proposed filtering scheme has been successfully applied to the denoising of the cDNA microarray images. Experimental results proved that the new filter is capable of removing efficiently the impulses present in multichannel images, while preserving their textural features.