Vignetting refers to the fall-off pixel intensity from the centre towards the edges of the image. This effect is undesirable in image processing and analysis. In the literature, the most commonly used methods of vignetting correction assume radial characteristic of vignetting. In the case of camera lens systems with non-radial vignetting, such approach leads to insufficient correction. Additionally, the majority of vignetting correction methods need a reference image acquired from a uniformly illuminated scene, what can be difficult to achieve. In this paper, we propose a new method of vignetting correction based on the local parabolic model of non-radial vignetting and compensation of non-uniformity of scene luminance. The new method was tested on camera lens system with non-radial vignetting and non-uniformly illuminated scene. In these conditions, the proposed method gave the best correction results among the tested methods.

Image vignetting is one of the major radiometric errors that occur in lens-camera systems. In many applications, vignetting is an undesirable effect; therefore, when it is impossible to fully prevent its occurrence, it is necessary to use computational methods for its correction. In probably the most frequently used approach to the vignetting correction, that is, the flat-field correction, the use of appropriate vignetting models plays a pivotal role. The radial polynomial (RP) model is commonly used, but for its proper use, the actual vignetting of the analyzed lens-camera system has to be a radial function. However, this condition is not fulfilled by many systems. There exist more universal models of vignetting; however, these models are much more sophisticated than the RP model. In this article, we propose a new model of vignetting named the Deformable Radial Polynomial (DRP) model, which joins the simplicity of the RP model with the universality of more sophisticated models. The DRP model uses a simple distance transformation and minimization method to match the radial vignetting model to the non-radial vignetting of the analyzed lens-camera system. The real-data experiment confirms that the DRP model, in general, gives better (up 35% or 50%, depending on the measure used) results than the RP model.

Color image quantization has become an important operation often used in tasks of color image processing. There is a need for quantization methods that are fast and at the same time generating high quality quantized images. This paper presents such color quantization method based on downsampling of original image and K-Means clustering on a downsampled image. The nearest neighbor interpolation was used in the downsampling process and Wu’s algorithm was applied for deterministic initialization of K-Means. Comparisons with other methods based on a limited sample of pixels (coreset-based algorithm) showed an advantage of the proposed method. This method significantly accelerated the color quantization without noticeable loss of image quality. The experimental results obtained on 24 color images from the Kodak image dataset demonstrated the advantages of the proposed method. Three quality indices (MSE, DSCSI and HPSI) were used in the assessment process.

Objective image quality assessment (IQA) measures are playing an increasingly important role in the evaluation of digital image quality. New IQA indices are expected to be strongly correlated with subjective observer evaluations expressed by Mean Opinion Score (MOS) or Difference Mean Opinion Score (DMOS). One such recently proposed index is the SuperPixel-based SIMilarity (SPSIM) index, which uses superpixel patches instead of a rectangular pixel grid. The authors of this paper have proposed three modifications to the SPSIM index. For this purpose, the color space used by SPSIM was changed and the way SPSIM determines similarity maps was modified using methods derived from an algorithm for computing the Mean Deviation Similarity Index (MDSI). The third modification was a combination of the first two. These three new quality indices were used in the assessment process. The experimental results obtained for many color images from five image databases demonstrated the advantages of the proposed SPSIM modifications.

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.