Інформаційна технологія стилізації та колоризації зображень

Nowdays due to the efficient algorithms and architectures of convolutional neural networks, as well as the possibility of applying the transfer learning approach, there are significant advances in various areas of automated image processing from accurate object detection to more creative areas such as colorization or style transfer. Given the practical significance of these tasks in various fields of human activity, it is important to create information technology and software to ensure effective and high-quality processing of visual content, as well as improving existing algorithms and approaches to obtain results that best meet expectations. In this paper the information technology of stylization and colorization of images with a possibility of both automated, and thin adjustment of parameters according to user preferences has been created. For the stylization task, it is possible to save the original colors of the content image, the transfer of style in this case is performed only in the brightness channel. This is motivated by the observation that visual perception is much more sensitive to changes in brightness than in color. Improving the detail and sharpness of the resulting images has been done by reducing the noise by the method of Total Variation, which allows, reducing the noise, to keep the edges and contours of the image unchanged.  In addition, the proposed technology realizes the possibility of increasing the image resolution in the context of a stand-alone task, and it is shown that  using it as a preliminary step of colorization can improve the clarity of images and the quality of the results. The proposed technology is implemented in the author's software, using Python programming language and the Tensorflow library.Разработана информационная технология стилизации и колоризации изображений с возможностью детальной настройки параметров на основе применения искусственных сверточных нейронных сетей и подхода transfer learning. Предложен метод повышения четкости получаемых после преобразования изображений.Розроблено інформаційну технологію стилізації та колоризації зображень з можливістю детального налаштування параметрів на основі застосування штучних згорткових нейронних мереж та підходу transfer learning. Запропоновано метод підвищення чіткості отримуваних після перетворення зображень

A fast one dimensional total variation regularization algorithm

Denoising has numerous applications in communications, control, machine learning, and many other fields of engineering and science. A common way to solve the problem utilizes the total variation (TV) regularization. Many efficient numerical algorithms have been developed for solving the TV regularization problem. Condat described a fast direct algorithm to compute the processed 1D signal. In this paper, we propose a variant of the Condat’s algorithm based on the direct 1D TV regularization problem. The usage of the Condat algorithm with the taut string approach leads to a clear geometric description of the extremal function.The work was supported by Russian Science Foundation grant №15-19-10010

A novel switching bilateral filtering algorithm for depth map

In this paper, we propose a novel switching bilateral filter for depth map from a RGB-D sensor. The switching method works as follows: the bilateral filter is applied not at all pixels of the depth map, but only in those where noise and holes are possible, that is, at the boundaries and sharp changes. With the help of computer simulation we show that the proposed algorithm can effectively and fast process a depth map. The presented results show an improvement in the accuracy of 3D object reconstruction using the proposed depth filtering. The performance of the proposed algorithm is compared in terms of the accuracy of 3D object reconstruction and speed with that of common successful depth filtering algorithms.The Russian Science Foundation (project #17-76-20045) financially supported the work