A POCS Algorithm For Super-resolution Image Reconstruction

Peop1e always need to obtain the best quality images in many situations. But in the actual imaging process,the image is degraded by the restricts with imaging system,the external environment and the imaging technology and so on,and the images that we obtained are not certainly ideal. However,in the practical application,the quality of the image is required higher and higher. Therefore,how to restore the primitive image effectively or retreat the image that we expect becomes an important problem in image process. Deteriorative images are caused by motion blurring, point spread blurring, under-sampling and sensor noise in imaging process. In order to amend the degrading quality and improve the image resolution, the super-resolution(SR) restoration technique based on multi-imaging is employed frequently. Super-resolution image restoration is a method that combines multiple similar but not identical low-resolution images into a higher resolution single image. In a particular digital imaging system, using super-resolution methods can help to obtain higher quality images without upgrading system hardware, therefore super-resolution techniques can be applied to vast image-processing areas.Super-resolution techniques can be divided into two main categories: frequency domain methods and spatial domain methods. Frequency domain methods are earlier super-resolution methods, they can only deal with image sequences that only translational motions are allowed. Spatial methods use general observation models, they have better adaptability and performance. In POCS method, projection onto convex sets theories is employed to realize super-resolution restoration, it is intuitive in theoretical and has good reconstruction performance. POCS is one of the most promising super-resolution restoration methodsThe reconstruction algorithms both from single low resolution( LR) image and a sequence of LR images are deeply researched in this thesis. In single LR image reconstruction. First introduce the traditional interpolation, then mainly research the method of super-resolution image reconstruction based on wavelet. According to characteristic of image wavelet transform and interpolation,this paper proposes an image interpolation method combining wavelet transform and interpolation algorithm. The experimental results show the computational feasibility of this method.In sequence LR images reconstruction, the research work in this paper focuses on POCS, analyzing the factors which influence the results of super resolution, such as motion estimation, the times of circulation and the threshold. According to the traditional super-resolution image reconstruction algorithm led to the blur edge of reconstructed high image. With an analysis of the reason which led to the blur edge of high-resolution reconstruction image , a new image interpolation based on gradient is proposed, it kept large information of the image edge. Use this new image interpolation to get the original value of the POCS. The experimental tests show that this algorithm can significantly improve the quality of the reconstruction image.