Marek Szczepanski

Self-adaptive algorithm of impulsive noise reduction in color images

In this paper a new approach to the problem of impulsive noise reduction in color images is presented. The basic idea behind the new image filtering technique is the maximization of the similarities between pixels in a predefined filtering window. The improvement introduced to this technique lies in the adaptive establishing of parameters of the similarity function and causes that the new filter adapts itself to the fraction of corrupted image pixels. The new method preserves edges, corners and fine image details, is relatively fast and easy to implement. The results show that the proposed method outperforms most of the basic algorithms for the reduction of impulsive noise in color images.

Towards automatic redeye effect removal

The redeye effect is typically formed in amateur photographs taken with a built-in camera flash. Analysis of the available techniques and products indicates that their efficiency in correcting this artifact is limited and their performance is inconsistent. In this work we propose a user friendly solution, which could be used to restore amateur photographs. In the proposed method the redeye effect is detected using a skin detection module and eye colors are restored using morphological image processing. The new method is computationally efficient, robust to parameter settings and versatile, as it can work in conjunction with a number of skin detection methods.

On the geodesic paths approach to color image filtering

In this paper a novel method of noise reduction in color images is presented. The class of filters introduced here utilizes fuzzy membership functions defined over vectorial inputs connected via digital geodesic paths. The efficiency of the new filters is compared under a variety of performance criteria with the commonly used filters, such as the vector median and the generalized vector directional filter. It is shown that, compared to existing techniques, the filters introduced here are better able to suppress impulsive, Gaussian as well as mixed-type noise. Furthermore, the computational analysis included in this work shows that some members of the new filter family are computationally less demanding than the vector median filter.

On the distance function approach to color image enhancement

A new class of image processing filters is introduced and analyzed in this paper. The new filters utilize fuzzy measures applied to image pixels connected by digital paths. The performance of the proposed filters is compared to the performance of commonly used filters, such as the vector median, under a variety of performance criteria. It is shown that the proposed filters are better able to suppress impulsive and Gaussian noise than the existing techniques. Also, they are robust to inaccuracies in parameter settings.

On the modified weighted vector median filter

A new filtering approach designed to eliminate impulsive noise in color images, while preserving fine image details is presented. The computational complexity of the new filter is significantly lower than that of the weighted vector median filter (WVMF). The comparison shows that the new filter outperforms the WVMF, as well as other standard procedures used in color image filtering for the removal of impulsive noise.

Fast Modified Vector Median Filter

A new filtering approach designed to eliminate impulsive noise in color images, while preserving fine image details is presented in this paper. The computational complexity of the new filter is significantly lower than that of the Vector Median Filter. The comparison shows that the new filter outperforms the VMF, as well as other standard procedures used in color image processing, when the impulse noise is to be eliminated.

Enhancement of the DNA microarray chip images

In this paper a novel method of noise reduction in color images is presented. The new technique is capable of attenuating both impulsive and Gaussian noise, while preserving and even enhancing the sharpness of the image edges. Extensive simulations reveal that the new method outperforms significantly the standard techniques widely used in multivariate signal processing. In this work we apply the new noise reduction method for the enhancement of the images of gene chips. We demonstrate that the new technique is capable of reducing various kinds of noise present in microarray images and that it enables efficient spot location and estimation of the gene expression level, due to the smoothing effect and preservation of the spot edges. This paper contains the comparison of the new technique of noise reduction with the standard procedures used for the processing of vector valued images, as well as examples of the efficiency of the new algorithm when applied to typical microarray images.

On the fast modified vector median filter

A new filtering approach designed to eliminate impulsive noise in color images, while preserving fine image details is presented in this paper. The computational complexity of the new filter is lower than that of the vector median. The comparison shows that the new filter outperforms the VMF, as well as other standard procedures used in color image processing, when the impulse noise is to be eliminated.

Developing a denoising filter for electron microscopy and tomography data in the cloud

The low radiation conditions and the predominantly phase-object image formation of cryo-electron microscopy (cryo-EM) result in extremely high noise levels and low contrast in the recorded micrographs. The process of single particle or tomographic 3D reconstruction does not completely eliminate this noise and is even capable of introducing new sources of noise during alignment or when correcting for instrument parameters. The recently developed Digital Paths Supervised Variance (DPSV) denoising filter uses local variance information to control regional noise in a robust and adaptive manner. The performance of the DPSV filter was evaluated in this review qualitatively and quantitatively using simulated and experimental data from cryo-EM and tomography in two and three dimensions. We also assessed the benefit of filtering experimental reconstructions for visualization purposes and for enhancing the accuracy of feature detection. The DPSV filter eliminates high-frequency noise artifacts (density gaps), which would normally preclude the accurate segmentation of tomography reconstructions or the detection of alpha-helices in single-particle reconstructions. This collaborative software development project was carried out entirely by virtual interactions among the authors using publicly available development and file sharing tools.

Forward and backward anisotropic diffusion filtering for color image enhancement

A novel approach to the problem of edge preserving smoothing, which allows an image to be broken into a set of homogeneous regions, is proposed and evaluated. The new algorithm is based on combined forward and backward anisotropic diffusion with an incorporated time dependent cooling process. This method is able to remove image noise efficiently while preserving and enhancing image edges.