Super-resolution Image Reconstruction Via Wavelet

Recent years have seen growing interest in studying of super-resolution image reconstruction technology, which refers to image processing algorithms to reconstruct high quality and high-resolution(HR) images from a sequence of degraded low-resolution(LR) images. Technology of super-resolution has been widely used in remote sensing military surveillance and medical diagnosis, etc.The method of super-resolution image reconstruction based on wavelet is considered in this paper. Firstly, we introduce the background basic principle and current method of super-resolution image reconstruction algorithm, and super-resolution models have being established. Secondly, we study the preprocessing part of system, a method of bidirectional multi-resolution motion estimation via radon transform is proposed, which can be used to estimate both pixel and subpixel motion vectors with robustly accurately and efficiently. Bad frames elimination via the variances of LR frames in the sequence is preformed before the implementation of the super-resolution algorithm. Thirdly, principle and structure of the core super-resolution image reconstruction algorithm based on wavelet has been studied. Some problems with implementation have been solved by some key tools. Lastly, the desirable properties of wavelet for wavelet super-resolution algorithm are considered to choose suitable wavelet. And noise filtering issues based on wavelet have been taken into account during implementation.