Robust Wavelet-Based Super-Resolution Reconstruction: Theory and Algorithm
We present an analysis and algorithm for the problem of super-resolution imaging, which is the reconstruction of high-resolution (HR) images from a sequence of low-resolution (LR) images. Super-resolution reconstruction entails solutions to two problems. One is the alignment of image frames. The other is the reconstruction of an HR image from multiple aligned LR images. Both are important for the performance of super-resolution imaging. Image alignment is addressed with a new batch algorithm, which simultaneously estimates the homographies between multiple image frames by enforcing the surface normal vectors to be the same. This approach can handle longer video sequences quite well. Reconstruction is addressed with a wavelet-based iterative reconstruction algorithm with an efficient denoising scheme. The technique is based on a new analysis of video formation. At a high level, our method could be described as a better-conditioned iterative back-projection scheme with efficient regularization criteria in each iteration step. Experiments with both simulated and real data demonstrate that our approach has better performance than existing super-resolution methods. It can remove even large amounts of mixed noise without creating artifacts.
super-resolution perfect reconstruction filter banks multiple frame alignment wavelet denoising