Research On Dual Image Super-resolution Based On The Mixed Priori Model

Super-resolution image reconstruction (SRR) refers to a resolution enhancement technology that extracting image information from several low-resolution (LR) images. It can eliminate various noise and blur which affect the quality of the image, rebuilding a higher resolution image with more clearly quality. Mathematically, super resolution is an inherently ill-posed inverse problem and is one of the most challenging problems in Computer Vision. In some real-life applications, it is very important to study the super-resolution on the dual images.This dissertation reviews the super resolution approaches, then presents a new robust super resolution algorithm of dual images which is based on the mixed priori model. This paper first introduces the origin of the dual image super-resolution model, then solves and estimates the model parameters, thus completes the dual image super-resolution process.The specific research in this paper has the following details:1. It presents a new latent image prior, which has three components:the global prior, which leads to edge-preserving SR images; the local prior, which leads to SR images with less ringing artifacts, and the displacement prior.2. Taking a semi-empirical solving solution to estimate Gaussian blur kernel instead of the method of taking the fixed Gaussian blur kernel. And the final result is more applicable to the current image.3. By using the IRLS approach to get a robust cost function to reject outliers, the SR results with little artifacts have been achieved.