ASCI2004_TPLV - Super-resolution Fusion using Adaptive...

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Unformatted text preview: Super-resolution Fusion using Adaptive Normalized Averaging Tuan Q. Pham Lucas J. van Vliet Quantitative Imaging Group, Faculty of Applied Sciences, Delft University of Technology, Lorentzweg 1, 2628 CJ Delft, The Netherlands { tuan, lucas } @ph.tn.tudelft.nl www.ph.tn.tudelft.nl/ ∼ lucas Keywords: super-resolution, sampling density, adaptive normalized averaging, irregular samples Abstract A fast method for super-resolution (SR) recon- struction from low resolution (LR) frames with known registration is proposed. The irregular LR samples are incorporated into the SR grid by stamp- ing into 4-nearest neighbors with position certainties. The signal certainty reflects the errors in the LR pix- els’ positions (computed by cross-correlation or optic flow) and their intensities. Adaptive normalized aver- aging is used in the fusion stage to enhance local lin- ear structure and minimize further blurring. The local structure descriptors including orientation, anisotropy and curvature are computed directly on the SR grid and used as steering parameters for the fusion. The optimum scale for local fusion is achieved by a sam- ple density transform, which is also presented for the first time in this paper. 1 Introduction Super-resolution from a sequence of low resolu- tion images is an important step in early vision to in- crease spatial resolution of captured images for later recognition tasks. An extensive literature on this topic exists [1] [2], of which there are two main approaches: one with simultaneous estimation of image registra- tion parameters and the high resolution image [3] [4] and the other with registration being computed sepa- rately before fusion [5] [6]. The super-resolution al- gorithm presented in this paper follows the second ap- proach. In other words, given an estimate of the regis- tration, our method attempts to reconstruct a high res- olution image that is visually more informative than each of the individual low resolution frames. The main tasks involved in the fusion process are first to map the irregular pixels onto a rectangularly sampled SR grid and secondly to derive the SR grid values from them. The first attempt of such was initi- ated by Tsai and Huang [7], who assumed the aliased and noise-free low resolution images were originally sampled from a band-limited high resolution image. Kim, Bose and Valenzuela [5] extended this work to handle noisy and blurred LR images using recursive least squares optimization in the Fourier domain. A spatial domain method was then reported by Ur and Gross [8]. However, all these early algorithms only works with shifted LR inputs that can be aligned peri- odically within the SR grid. In a recent paper [6], Ler- trattanapachich and Bose presented a spatial domain method based on Delaunay triangulation [9] that al- lows any arbitrary organization of input samples. Our structure adaptive fusion algorithm in this paper also can deal with irregular registration after a process of stamping pixels back onto the grid.stamping pixels back onto the grid....
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ASCI2004_TPLV - Super-resolution Fusion using Adaptive...

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