Henryk Palus

Edge Preserving Filters on Color Images

In this contribution we present experiments on color image enhancement for several different non-linear filters which originally were defined for gray-level images. We disturb sample images by different types of noise and measure performance of the filters. We provide signal-to-noise measurements as well as perceived color difference in ΔE as defined by the CIE. All images and test programs are provided online on the internet so that experiments can be validated by arbitrary users on any image data.

KM and KHM Clustering Techniques for Colour Image Quantisation

This paper deals with the comparison of two clustering techniques kmeans (KM) and k-harmonic means (KHM) in the case of their use in colour image quantisation. The classical KMtechnique establishes good background for this comparison. Authors proposed two original heuristic initialisation methods, one arbitrary(DC) and one adaptive (SD), that were used in both techniques. Apart from specific validity indices for clustering, the results were also evaluated by means of average colour differences in RGB (PSNR) and CIELAB colour space (ΔE) and additionally difference of colourfulness (ΔM). Experimental tests realised on benchmark colour images show the superiority of KHMover KM. Other problems with both clustering techniques e.g. empty clusters have also been highlighted.

Real-time detection and filtering of eye movement and blink related artifacts in EEG

Eye movement related artifacts are the most significant source of noise in EEG signals. Thus, a special approach to reduction of their influence is required. However, most of currently used methods of detecting and filtering eye movement related artifacts require either an additional recording of noise signal, or are not suitable for real time applications, such as Brain-Computer Interfaces. In this paper it was proven that it is possible to detect and filter those artifacts in real time, without the need of providing an additional recording of noise signal.

Colorfulness of the image: definition, computation, and properties

In this paper we show different methods of defining and computing of colofulness of the image from digital image processing point of view. All experiments have been carried out on the set of natural color images with different perceptual colorfulness. We have tested the images using following simple colorfulness estimate based on statistical parameters of the pixel cloud along red-green and yellow-blue axes. During image processing the colorfulness of the image can be changed by increasing after color enhancement or by decreasing after image compression. Sometimes the colorfulness of the image should be invariant. We have presented it on examples, which show that the colorfulness can be useful for evaluating the color quantization algorithms beside such traditional performance functions as RMSE and ΔE .

Real-Time Detection and Filtering of Eye Blink Related Artifacts for Brain-Computer Interface Applications

Artifacts related with eye movements are the most significant source of noise in EEG signals. Although there are many methods of their filtering available, most of them are not suitable for real-time applications, such as Brain-Computer Interfaces. In addition, most of those methods require an additional recording of noise signal to be provided. Applying filtering to the recorded EEG signal may unintentionally distort its uncontaminated segments. To reduce that effect filtering should be applied only to those parts of signal that were marked as artifacts. In this paper it was proven that it is possible to detect and filter those artifacts in real-time, without the need of providing an additional recording of noise signal.

Clustering with K-Harmonic Means Applied to Colour Image Quantization

The main goal of colour quantization methods is a colour reduction with minimum colour error. In this paper were investigated six following colour quantization techniques: the classical median cut, improved median cut, clustering k-means technique in two colour versions (RGB, CIELAB) and also two versions of relative novel technique named k-harmonic means. The comparison presented here was based on testing of ten natural colour images for quantization into 16, 64 and 256 colours. In evaluation process two criteria were used: the mean squared quantization error (MSE) and the average error in the CIELAB colour space (DeltaE). During tests the efficiency of k-harmonic means applied to colour quantization has been proved.

Colourfulness of the image and its application in image filtering

In this paper we show, from image processing point of view, different methods of computing of colourfulness of the image. We have calculated the colourfulness using simple estimate based on statistical parameters of the pixel cloud along red-green and yellow-blue axes. All experiments have been carried out on the set of natural colour images with different perceptual colourfulness. The relationships between colourfulness of the image and perceptual attributes (H, L, S) of pixels have been experimentally investigated. During image processing the colourfulness of the image can be changed but sometimes it should be preserved e.g. in image filtering. We have presented it on examples, which show that the difference of colourfulness can be useful for evaluating the image filtering algorithms beside such traditional performance functions as PSNR and DeltaE

New image quality metric used for the assessment of color quantization algorithms

Color quantization is an important operation in the field of color image processing. In this paper, we consider a usefulness of the new DSCSI metric to assessment of quantized images. This metric is shown in the background of other useful image quality metrics to evaluate the color image differences and it has also been proven that DSCSI metric achieves the highest correlation coefficients with MOS. For further veriffcation DSCSI metric the combined methods that use to initialize the results of well-known splitting algorithms such as POP, MC, Wu etc. were tested. Experimental results of such combined methods indicate that the Wu+KM approach leading to the best quantized images in the sense of DSCSI metric.

KHM clustering technique as a segmentation method for endoscopic colour images

KHM clustering technique as a segmentation method for endoscopic colour images In this paper, the idea of applying the k-harmonic means (KHM) technique in biomedical colour image segmentation is presented. The k-means (KM) technique establishes a background for the comparison of clustering techniques. Two original initialization methods for both clustering techniques and two evaluation functions are described. The proposed method of colour image segmentation is completed by a postprocessing procedure. Experimental tests realized on real endoscopic colour images show the superiority of KHM over KM.

Toward a perceptual image quality assessment of color quantized images

Color image quantization is an important operation in the field of color image processing. In this paper, we consider new perceptual image quality metrics for assessment of quantized images. These types of metrics, e.g. DSCSI, MDSIs, MDSIm and HPSI achieve the highest correlation coefficients with MOS during tests on the six publicly available image databases. Research was limited to images distorted by two types of compression: JPG and JPG2K. Statistical analysis of correlation coefficients based on the Friedman test and post-hoc procedures showed that the differences between the four new perceptual metrics are not statistically significant.