The Research On Learning-based Image Super Resolution And Application

The main objective of super-resolution (SR) imaging is to reconstruct a higher-resolution image based on an image or a set of images, acquired from the same scene(similar information but different details) and denoted as'low-resolution'images. It overcomes the limitation ill-posed conditions of the image acquisition process, can get a better image content and improves a scene recognition. This technology is widely used in remote sensing recognition, image compression, high-definition television, security monitoring, video communications, medical diagnostics, resource exploration and many other fields, is one of the most popular subjects in the current field of image processing and computer vision research, and has a very important theoretical research value.On the base of studies of the learning-based image super-resolution algorithms and the gradient-based image super-resolution algorithms, this paper proposes a learning-based single-frame image super-resolution algorithm combined with the information of edges and details under the energy optimization framework.Firstly, we briefly introduce the concept, the background of image super-resolution and then nearly a decade of significant domestic and international developments of it in this article. Secondly, we introduce the theoretical foundation of super-resolution technology, a variety of algorithms in the spatial domain, and a brief analysis of the advantages and disadvantages of these algorithms. At the same time we use Matlab tools to achieve these correlation algorithms, and gain the enlarged images. Then we study the example-based super-resolution through a relationship of the high-resolution images and the low-resolution images learned by the Markov network and the gradient-based super-resolution algorithm using the prior knowledge of image gradient, and then by the actual programming of these two algorithms to obtain the corresponding experimental results. Through theoretical analysis of experimental results that can be combined the information of edge and detail to improve the quality of the high-resolution image. Lastly, we propose a new single-frame image super-resolution algorithm.This paper focuses on the learning-based single-frame image super-resolution algorithm under the framework of energy optimization, section 3.3.1 gives a model of super-resolution, the definition of the energy equation and its solving method, section 3.3.2 introduces the own training set of images and section 3.3.3 the search methods, then section 3.3.4 shows the flow chart and pseudo code of the super-resolution algorithm. From the experimental results we can see that the obtained high-resolution images by the proposed algorithm are improved on the edge and detail, with a better visual. Finally, we summarize the content of this study, the problems to be solved that super-resolution has faced , the priorities of future research and prospect its future development direction.