Noise reduction is one of the most important and still active research topics in low-level image processing due to its high impact on object detection and scene understanding for computer vision systems. Recently, we observed a substantially increased interest in the application of deep learning algorithms. Many computer vision systems use them, due to their impressive capability of feature extraction and classification. While these methods have also been successfully applied in image denoising, significantly improving its performance, most of the proposed approaches were designed for Gaussian noise suppression. In this paper, we present a switching filtering technique intended for impulsive noise removal using deep learning. In the proposed method, the distorted pixels are detected using a deep neural network architecture and restored with the fast adaptive mean filter. The performed experiments show that the proposed approach is superior to the state-of-the-art filters designed for impulsive noise removal in color digital images.
The aim of the paper is to present the results of investigations concerning the implementation of pseudocolor visualization algorithm of segmented images, capable to find the color combination producing maximum contrast between the segmented areas. Very often there is a need of visualization of segmentation results and usually they are presented by assigning colors randomly or from predefined palettes, what could decrease the visualization effect, when neighboring regions have assigned similar colors. To alleviate this problem, we propose novel methodology for deriving optimized visualization based on maximizing local distance between colors. In the paper we present visualization results using a new color contrast measure optimized with a genetic algorithm and compare the effectiveness with a greedy algorithm. The proposed method can be used to obtain visually pleasing pseudocolor encoded images of segmentation results which can be useful for the presentation of various kinds of visual information.
Observations of the solar photosphere from the ground encounter significant problems caused by Earth's turbulent atmosphere. Before image reconstruction techniques can be applied, the frames obtained in the most favorable atmospheric conditions (the socalled lucky frames) have to be carefully selected. However, estimating the quality of images containing complex photospheric structures is not a trivial task, and the standard routines applied in nighttime lucky imaging observations are not applicable. In this paper we evaluate 36 methods dedicated to the assessment of image quality, which were presented in the literature over the past 40 years. We compare their effectiveness on simulated solar observations of both active regions and granulation patches, using reference data obtained by the Solar Optical Telescope on the Hinode satellite. To create images that are affected by a known degree of atmospheric degradation, we employed the random wave vector method, which faithfully models all the seeing characteristics. The results provide useful information about the method performances, depending on the average seeing conditions expressed by the ratio of the telescope's aperture to the Fried parameter, D/r 0 . The comparison identifies three methods for consideration by observers: Helmli and Scherer's mean, the median filter gradient similarity, and the discrete cosine transform energy ratio. While the first method requires less computational effort and can be used effectively in virtually any atmospheric conditions, the second method shows its superiority at good seeing (D/r 0 < 4). The third method should mainly be considered for the post-processing of strongly blurred images.
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