Robust Wavelet-Based Super-Resolution Reconstruction: Theory and Algorithm

• Published in: IEEE transactions on pattern analysis and machine intelligence Vol. 31; no. 4; pp. 649 - 660
• Main Authors: Hui Ji, Fermuller, C.
• Format: Journal Article
• Language: English
• Published: Los Alamitos, CA IEEE 01.04.2009; IEEE Computer Society; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithm design and analysis; Algorithms; Alignment; Applied sciences; Artificial intelligence; Computer science; control theory; systems; Detection, estimation, filtering, equalization, prediction; Enhancement; Exact sciences and technology; Frames; High-resolution imaging; Image analysis; Image processing software; Image reconstruction; Image resolution; Image sequence analysis; Imaging; Information, signal and communications theory; Intelligence; Iterative algorithms; Pattern analysis; Pattern recognition. Digital image processing. Computational geometry; Reconstruction; Robustness; Signal and communications theory; Signal, noise; Studies; Surface reconstruction; Telecommunications and information theory; Video sequences; Image processing software; Enhancement; High resolution; Perfect reconstruction filter banks; Image processing; Noise reduction; super-resolution; Iterative method; Artefact; Image reconstruction; Multiple image; Alignment; Image sequence; Imaging; Homograph; Batch production; Pattern analysis; Image resolution; multiple frame alignment; wavelet denoising; Low resolution; Surface normal; Image analysis; Wavelet transformation; Batch process; Artificial intelligence; Algorithm analysis; Signal to noise ratio
• ISSN: 0162-8828; 1939-3539
• DOI: 10.1109/TPAMI.2008.103

We present an analysis and algorithm for the problem of super-resolution imaging, that is the reconstruction of HR (high-resolution) images from a sequence of LR (low-resolution) images. Super-resolution reconstruction entails solutions to two problems. One is the alignment of image frames. The other is the reconstruction of a 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 de-noising 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 an 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.