The rapid growth of image archives increases the need for efficient and fast tools that can retrieve and search through large amount of visual data. In this paper we propose an efficient method of extracting the image color content, which serves as an image digital signature, allowing to efficiently index and retrieve the content of large, heterogeneous multimedia databases. We apply the proposed method for the retrieval of images from the WEBMUSEUM Internet database, containing the collection of fine art images and show that the new method of image color representation is robust to image distorsions caused by resizing and compression and can be incorporated into existing retrieval systems which exploit the information on color content in digital images.
The smoothing function of widely used vector filters such as vector median (VMF), basic vector directional filter (BVDF) and directional distance filter (DDF) is designed to perform the fixed amount of smoothing. It may become the undesired property, because in some image areas these filters introduce too much smoothing and blur thin details and image edges. In general, the common problem is how to preserve some desired signal features while the noise elements are removed. An optimal situation would arise if the filter could be designed so that the desired features were invariant to the filtering operation and only noise would be affected. In case of the impulsive noise corruption, the problem is stated often as searching for the switching function that allows to reduce the filter effect only to noisy samples. In this paper, a new nonlinear filtering scheme for the removal of impulsive noise in multichannel digital images is presented. A new class of multichannel sigma filters is based on the combination of the standard sigma-filter concept provided by Lee and the robust order-statistics theory. With respect to a variety of the measures (e.g. vector distance expressed through Minkowski metric, angular distance or their combination) for quantification of the distance between multichannel samples, we provide a rich class of adaptive vector sigma filters taking advantages of the threshold structure with the approximation of the standard deviation and also the fully adaptive filter structure. Thus, by adaptive switching between the smoothing function and the identity operation, the behavior of the proposed method is attractive for filtering of image environments degraded by impulsive noise, bit errors and outliers. The new filtering scheme is computationally efficient and able to achieve excellent balance between the image detail preservation and the noise suppression. The achieved results show that the new filtering class has excellent preservation capabilities and provides significant improvement in comparison with well-known vector filters such as VMF, BVDF and DDF in terms of all commonly used quality measures.
In this paper we address the problem of impulsive noise reduction in multichannel images. A new class of filters for noise attenuation is introduced and its relationship with commonly used filtering techniques is investigated. The computational complexity of the new filter is significantly lower than that of the Vector Median Filter,
(VMF). Extensive simulation experiments indicate that the new filter outperforms the VMF, as well as other techniques currently used to eliminate impulsive noise in color images.
We provide a unified framework of nonlinear vector techniques outputting the lowest ranked vector. The proposed framework constitutes a generalized filter class for multichannel signal processing. A new class of nonlinear selection filters are based on the robust order-statistic theory and the minimization of the weighted distance function to other input samples. The proposed method can be designed to perform a variety of filtering operations
including previously developed filtering techniques such as vector median, basic vector directional filter, directional distance filter, weighted vector median filters and weighted directional filters. A wide range of filtering operations is guaranteed by the filter structure with two independent weight vectors for angular and distance domains of the vector space. In order to adapt the filter parameters to varying signal and noise statistics, we provide also the generalized optimization algorithms taking the advantage of the weighted median filters and the relationship between standard median filter and vector median filter. Thus, we can deal with both statistical and deterministic aspects of the filter design process. It will be shown that the proposed method holds the required
properties such as the capability of modelling the underlying system in the application at hand, the robustness with respect to errors in the model of underlying system, the availability of the training procedure and finally, the simplicity of filter representation, analysis, design and implementation. Simulation studies also indicate that the new filters are computationally attractive and have excellent performance in environments corrupted by bit errors and impulsive noise.
This paper presents a new filtering scheme for the removal of impulsive noise in color images. It is based on estimating the probability density function for color pixels in a filter window by means of the kernel density estimation method. A quantitative comparison of the proposed filter with the vector median filter shows its excellent ability to reduce noise while simultaneously preserving fine image details.
In this paper a novel approach to the problem of edge preserving noise reduction in color images is proposed and evaluated. The new algorithm is based on the combined forward and backward anisotropic diffusion with incorporated time dependent cooling process. This method is able to efficiently remove image noise, while preserving and even enhancing image edges. The proposed algorithm can be used as a first step of different techniques, which are based on color, shape and spatial location information.
We provide a new non-motion compensated adaptive multichannel filter for the detection and removal of impulsive noise, bit errors and outliers in color video or color image sequences. The proposed nonlinear filter takes the advantages of the concept of the local entropy contrast and the robust order-statistics theory. The new entropy based vector median is computationally attractive, robust for a wide range of the impulsive noise corruption and significantly improves the signal-detail preservation capability of standard vector median filter. Because the precision of statistical operators such as mean or entropy increases with the increased number of observed samples, the used spatiotemporal cube filter window guarantees a high accuracy of the proposed method that is able to achieve excellent results in terms of commonly used objective measures and clearly outperforms standard vector filtering schemes