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AN ITERATIVE SUPER-RESOLUTION RECONSTRUCTION OF IMAGE SEQUENCES USING A BAYESIAN APPROACH AND AFFINE BLOCK-BASED REGISTRATION V. Patanavijit and S. Jitapunkul †† Department of Computer Engineering, Faculty of Engineering, Assumption University, Bangkok, Thailand †† Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand patanavijit@yahoo.com and †† somchai.j@chula.ac.th ABSTRACT Due to translational registration, traditional super-resolution re- constructions can apply only on the sequences that have simple translation motion. This paper reviews the super-resolution algo- rithm in these two decades and proposes a novel super-resolution reconstruction that that can apply on real sequences or complex motion sequences. The proposed super-resolution reconstruction uses a high accuracy registration algorithm, the fast affine block- based registration [42], in the stochastic regularization technique of Bayesian MAP estimation used to compensate the missing meas- urement information. The experimental results show that the pro- posed reconstruction can apply on real sequence such as Suzie, Mobile Calendar and Foreman. 1. INTRODUCTION Typically, theoretical and practical limitations constrain the achievable resolution of any devices. SR (Super-Resolution) image reconstruction algorithms investigate the relative motion informa- tion between multiple LR (Low Resolution) images (or a video sequence) and increase the spatial resolution by fusing them into a single frame. In doing so, it also removes the effect of possible blurring and noise in the LR images [7], [20], [22], [37]. Recent work relates this problem to restoration theory [34]. As such, the problem is shown to be an inverse problem, where an unknown image is to be reconstructed, based on measurements related to it through linear operators and additive noise. This linear relation is composed of geometric warp, blur and decimation operations. The super-resolution problem is modelled by using sparse matrices and analyzed from many reconstruction methods [20] such as the Non- uniform Interpolation, Frequency Domain, Maximum-Likelihood (ML), Maximum A-Posteriori (MAP), and Projection Onto Convex Sets (POCS). The super-resolution restoration idea was first presented by T. S. Huang and R. Y. Tsan [39] in 1984. They used the frequency do- main approach to demonstrate the ability to reconstruct one im- proved resolution image from several downsampled noise-free versions of it, based on the spatial aliasing effect. A frequency domain recursive algorithm for the restoration of super-resolution images from noisy and blurred measurements is proposed by S. P. Kim, N. K. Bose, and H. M. Valenzuela [31] in 1990. The algo- rithm using a weighted recursive least squares algorithm, is based on sequential estimation theory in the frequency-wavenumber do- main, to achieve simultaneous improvement in signal-to-noise ratio and resolution from available registered sequence of low-resolution noisy frames. In [32], S. P. Kim and Wen-Yu Su also incorporated
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