Research Of Face Image Super Resolution Algorithm Based On Learning

Research efforts in face processing always is one of a hot spot research questions which in the computer vision, the pattern recognition and in computer graphics domain. Nowadays, the human face image resolution which obtains through the monitoring device is not high, the human face recognition and the track and so on the following application brings the very great difficulty for the human. The Supper Resolution (SR)technology is one kind method obtains the high resolution(HR)image based on the signal processing technology . The basic idea behind SR is the fusion of a sequence of low-resolution noisy blurred images to produce a higher-resolution image based on the signal processing technology. The SR in human face research, long-distance image remote sensing, video frequency monitoring, and medicine domains and so on has the very good application.We mainly study the single face image super resolution techniques with the goal aiming at an algorithm which is more simple, practical and suitable for real-time applications. First, this article comprehensively reviewed and narrated and commented the SR technology concept, essential method and SR algorithm. In this foundation key research based on study image SR algorithm. This article used Markov network (MN) model to propose a new Baye (MAP) frame description restructuring mechanism. We propose a novel algorithm that uses the location-restraint operation and uses the most compatible neighboring patches along horizontal dimension of the face to directly mosaic the high-resolution patches into the outcome. This can reduce the search order of complexity, enhance the match relevance, speed up Markov network restraining, and simplify the implicit strata node computation. The tests platform by the VC++ programming developmented, the human face image training sets which in the experiment uses 24 gradation images. The experimental result confirmed this article proposed the algorithm has the output quality well, an efficiency more higher characteristic, has certain practical value.