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Super-resolution using a Wavelet-based Adaptive Wiener Filter

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2 Author(s)
Sadaka, N.G. ; Sch. of Electr., Comput., & Energy Eng., Arizona State Univ., Tempe, AZ, USA ; Karam, L.J.

In this paper, a Wavelet-based Adaptive Wiener Filter (WAWF) super-resolution (SR) algorithm is presented. A redundant discrete dyadic wavelet transform (DDWT) is applied to the input sequence to classify the LR frames into subbands of similar contextual information. The similar subbands from each LR frame are registered using subpixel motion and merged on the same HR grid to form HR subbands of similar contextual and statistical information. Then an adaptive Wiener filter approach is applied locally to each subband in order to interpolate the missing information on the HR gird. The weights of the filter are designed using a parametric circularly symmetric statistical model that adapts to the statistics and the spatial proximity of the neighboring wavelet coefficients. Simulation results show that the proposed WAWF SR algorithm results in a superior performance as compared to existing recent SR schemes.

Published in:

Image Processing (ICIP), 2010 17th IEEE International Conference on

Date of Conference:

26-29 Sept. 2010

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