## 1 Introduction  
The purpose of this paper is to provide a new method for generating the data from a large number of images. The method is based on the multifractal descriptors for the identification of different macroals. The proposed method is based on the multifractal spectrum calculated from the method of multifractal detrended fluctuation analysis (MF-DFA). A lightweight solution for the estimation of affine parameters in affine motion compensation is proposed in [4]. It tries to speed up the process by means of evaluating affine prediction when it is likely to bring no encoding efficiency benefit as well as estimating better initial values for the iteration process. The optical flow between the reference image and the current image is applied in order to estimate the best encoding mode and achieve a better initial estimation. In paper [5] an image registration algorithm based on convolutional neural network (CNN) and local homography transformation is proposed. It applies firstly a novel sample and label generation method based on Moving Direct Linear Transformation (MDLT). Later, the local homography matrices between the two images are estimated by means of the MDLT and finally the image registration can be realized. The Authors of [6] proposed a pre-classified deep-learning algorithm (MGEP-SRCNN) applying a Multi-label Gene Expression Programming (MGEP), which screens out a sample sub-bank with highrelevance to the target image before image block extraction, pre-classifies samples in a multi-label framework, and then performs nonlinear mapping and image reconstruction. The detection of suspicious behavior using video sequences in a CCTV video stream is the subject of [7]. The proposed method detected suspicious behavior with a temporal saliency map by combining the moving reactivity features of motion magnitude and gradient extracted by optical flow. The paper [8] is devoted to the problem of haze removal from a single image in real-time. For this purpose, a normalized pixel-wise dark-channel prior is applied. In order to solve some problems with computational cost normalized pixel-wise haze estimation without losing the detailed structure of, the transmission map is used. The Authors additionally proposed robust atmospheric-light estimation using a coarse-to-fine search strategy and down-sampled haze estimation. In [9] a stable sparse model with a non-tight frame (SSM-NTF) is proposed and a dictionary pair learning model to stably recover the signals is formulated. The approach is applied on various image restoration tasks such as denoising, super resolution and inpainting. The paper [10] provides a proposition of a stronger adaptive local dimming method with details preservation. The approach, combining the advantages of some existing methods and introducing the combination of the subjective and objective evaluation, obtains a stronger adaptation. Additionally, in the paper the bi-histogram equalization algorithm is developed and a new pixel compensation method is proposed. In [11] the Authors use image quality metrics to evaluate the performance of several image fusion techniques to assess the spectral and spatial quality of pan-sharpened images. They evaluated twelve pan-sharpening algorithms, and experimentally proved that the Local Mean and Variance Matching (IMVM) algorithm was the best in terms of spectral consistency and synthesis. The Authors of [12] propose a data-driven redundant transform based on Parseval frames (DRTPF) by applying the frame and its dual frame as the backward and forward transform operators, respectively. The proposed model combines a synthesis and an analysis sparse systems. In [13] the problem of bilingual scene text reading is considered. For this purpose, an octave convolution (OctConv) feature extractor and a time-restricted attention encoder-decoder module for end-to-end scene text reading are proposed and experimentally investigated.