The acquisition of complex data of the recorded scene is an important task for video analysis of the processes occurring. The use of IR images allows obtaining data on additional characteristics that are not visible in the optical range. The data obtained by IR sensors can be in the near and far range, which makes it possible to see objects in the dark or to obtain data on their temperature. In the second case, the boundaries of objects are vague and difficult to correlate with the usual optical ranges. To do this a combination of data obtained by a pair of cameras is used. In this paper, we propose using the algorithm for stitching IR images based on data obtained in the optical range. To this end, an approach will be applied that includes parallel analysis of data on: saliency maps; search for boundaries and key points; reduction of bit resolution of images with preservation of borders; image matching; filtering data and restoring the sharpness of object boundaries. As an example, the result of combining data obtained under poor lighting conditions and the results of combining television images will be presented.
Medical visualization and analysis of medical data is an actual direction. Medical images are used in microbiology, genetics, roentgenology, oncology, surgery, ophthalmology, etc. Initial data processing is a major step towards obtaining a good diagnostic result. The paper considers the approach allows an image filtering with preservation of objects borders. The algorithm proposed in this paper is based on sequential data processing. At the first stage, local areas are determined, for this purpose the method of threshold processing, as well as the classical ICI algorithm, is applied. The second stage uses a method based on based on two criteria, namely, L2 norm and the first order square difference. To preserve the boundaries of objects, we will process the transition boundary and local neighborhood the filtering algorithm with a fixed-coefficient. For example, reconstructed images of CT, x-ray, and microbiological studies are shown. The test images show the effectiveness of the proposed algorithm. This shows the applicability of analysis many medical imaging applications.