Research On The Algorithm Of Super-resolution Reconstruction Of Infrared Images Based On MAP

A lot of effective super-resolution methods have been presented in recent years. Super-resolution reconstruction has been widely used in infrared images. First, this paper provides an overview of the mathematic principle of super-resolution image reconstruction and introduces several common algorithms. Then, an analysis is made on image processing of real images. According to motion estimation algorithm, subsampling model and back-projection algorithm, observation model is presented to simulate the real imaging process. Finally, based on the basic theory of Bayes MAP estimation and the prior model of Markov Random Field (MRF) and Gibbs Random Field (GRF), a fast and robust projection super-resolution algorithm based on Maximum a Posteriori (MAP) estimation is proposed to obtain a high resolution image from a set of infrared images, which are obtained by an uncooled infrared detector. Comparisons and analyses are made of the super-resolution reconstruction results by this method, with the variance of regularizations and the number of low resolution infrared images, by direct observation and the value of Power Signal-to-Noise Ratio (PSNR). Simulation results with several real sets of infrared images show the effectiveness and superiority of this method for enhancing resolution of infrared images.