Image Super Resolution using Fast Converging Iterative Interpolation and Back Projection

Super resolution (SR) is one of the techniques to enhance image resolution in terms of the number of pixels and noise reduction. In SR techniques, a sequence of low resolution (LR) images captured by moderate camera is used to generate a high resolution (HR) image. In this thesis a new Iterative Back Projection (IBP) based SR technique is proposed. In the proposed techniques, IBP technique is improved by using interpolation. This SR technique is achieved by adding an up-sampling and down sampling in each iteration. First of all, four observed LR images are generated by an observation LR model. One of these LR images is considered as a reference image, then interpolation techniques are used to increase the size of the reference image to the size of the ground truth image. This image is considered as an initial guess image. Then the size of initial image is increased and decreased respectively by using interpolation techniques. The interpolated image is decimated to four LR images. The LR images are registered to generate an HR image, then the HR image is sent back to the first step. This process is repeated iteratively until an error criterion is met. The proposed technique is called Iterative Interpolation and Back Projection (IIBP), since an interpolation (up-sampling followed by down-sampling) module is embedded in each iteration. The embedded interpolation module speeds up the convergence of the standard IBP and generates faster results than the standard IBP. The proposed techniques are tested on various well-known benchmark images. The quantitative Peak Signal-to-Noise Ratio (PSNR) results as well as the visual results show the superiority of the proposed techniques over the standard IBP whenever limited numbers of iterations are allowed. As the number of iterations approach to infinity, generally standard IBP gives better results. Keywords: Super Resolution, Iterative Back Projection, Image Registration