## 1 Introduction
The next contribution describes ideas of automatic cell segmentation and counting, which is an important problem in the analysis of microscopic images [14]. For this purpose fundamental yet effective image processing algorithms were applied. In [15] the automatic recognition of leaf images is considered. The applied approach is based on a Dual-output pulse-coupled neural network and Bag of features. Additionally, Bag of contour fragments was applied for shape feature extraction. Finally, Linear Discriminant Analysis was used for feature dimensionality reduction, and a Linear Support Vector Machine for classification. The proposed approaches were experimentally investigated using several leaf image datasets. The application of machine learning for the extraction of information from historical documents was the subject of works described in [16]. Because of the character of the data being investigated, (numerals were written in red) firstly a red color filter was applied in order to separate numerals from documents, and then a CNN-based segmentation method for spotting these numerals. The Authors of [17] used a fine-tuning method for image classification of large-scale remote sensing datasets. The approach applies feature extraction from the fine-tuned neural networks, and remote sensing image classification with a Support Vector Machine model with linear and Radial Basis Function kernels. In [18] a hybrid network based on an attention mechanism for stereoscopic salient object detection was proposed. It was combined with an encoder-decoder network. The described novel attention model is based on the fusion of RGB and depth attention maps. The problem of small objects and objects with large scale variants detection was analyzed in [19]. For solving this problem, an approach based on multi-scale balanced sampling was proposed. The submission [20] discusses the problems connected with the segmentation of similar phases in different ironmaking feedstock materials by means of automated optical image analysis and provides the description of the algorithms designed for textural identification. In [21] the idea of applying simple shape features for action recognition based on binary silhouettes was proposed. It was shown that basic shape features can discriminate between short, primitive actions performed by a single person. The last paper [22] provides a review of the approaches applied for dental images. The goal of the Authors was the description of the state of the art of artificial intelligence in dental applications, such as the detection of teeth, caries, filled teeth, crown, prosthesis, dental implants and endodontic treatment. The above brief description of the contributions provides the conclusion that the possibilities of applying image processing, analysis and recognition techniques for various problems are wide. In the papers accepted for publication in the Special Issue the analysis of high resolution satellite [1; 11], aerial [17], microscopic [3; 14], optical [20] and dental [22] images was provided. Image registration [5], restoration [6; 9], fusion [11], and denoising [12] were also taken into account as well as the haze removal from images [8], backlight extraction [10], pan sharpening [1; 2] and object detection [18; 19]. Some specific applications were also considered--text detection and recognition from natural images [13], leaf image recognition [15] and historical document analysis [16]. Finally, not only still images were processed. Video coding [4] and the analysis of video sequences for suspicious behavior detection [7] and action recognition [21] were analyzed as well. The Guest Editor is thankful for the valuable contributions from the authors, and significant help of reviewers, the editor team of Applied Sciences, and especially [PERSON] (Section Managing Editor). The authors declare no conflict of interest.