Regression and edge preservation based face image super-resolution

Abstract

The basic objective of Super Resolution (SR) techniques are to generate a High Resolution (HR) image from one or more Low Resolution (LR) images. Due to its vast applications in real world SR is becoming a very active research area and many SR methods has been proposed in last decade or so. Generally the SR methods can be divided into three categories Learning based methods, Interpolation based methods and Reconstruction based methods. In this thesis, a comprehensive study is done to get a better understanding of benefits and shortcomings of these categories. A position patch based SR method is also proposed combining the positive effects of all three categories. The proposed approach take the advantage of non-local self similarity of image to fill the missing pixels in HR image. Non local similarity uses the redundant frequency information in image whether it is local or faraway from the observed patch in that image. First an image is decomposed into many small patches, then the image is searched for similar patches. After searching the similarity in patches relative to each patch, the HR image is then reconstructed by filling the missing pixels using information present in similar patches. The main focus in this method is to preserve the edges in HR reconstructed image because most methods fails to perform with the same efficiency around edges of an image compare to their performance in rest of the image. The results show significant improvement in the quality of image. An enhancement filter is also applied on the final image to get the best results. A framework for given work is discussed in detail. The results indicate significant improvement in sharpness and resolution of image. Also the jagging effect around edges is reduced using proposed technique. The experimental analysis between proposed and state of the art methods shows the effectiveness of proposed approach. For quantitative analysis, two techniques PSNR and SSIM are used to validate the obtained results. For training set, fei database is used. The current methods fail to perform well when image is different from training sets which is why different datasets are used for testing purposes.