ICIP 2006, Atlanta, GA
 

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Paper:WA-P7.1
Session:Interpolation, Super-Resolution and Demosaicking
Time:Wednesday, October 11, 09:40 - 12:20
Presentation: Poster
Topic: Interpolation and Super-Resolution: Interpolation
Title: SPATIALLY CONSTRAINED WIENER FILTER WITH MARKOV AUTOCORRELATION MODELING FOR IMAGE RESOLUTION ENHANCEMENT
Authors: Jiazheng Shi; University of Michigan-Ann Arbor 
 Stephen Reichenbach; University of Nebraska-Lincoln 
Abstract: This paper develops a practical method for image resolution enhancement. The method optimizes the spatially constrained Wiener filter for an efficiently parameterized model of the image autocorrelation based on a Markov random field (MRF) with affine transformation. The paper presents a closed-form solution to parameterize the model for an image. The enhancement method is computationally efficient, because it is formulated as convolution with a small kernel. Because the kernel is small, it can be optimized efficiently and only a small portion of the MRF autocorrelation model is required. Because the autocorrelation model parameters and optimal filter can be computed quickly, the method can be optimized locally for adaptive processing. Experimental results indicate that the new method can balance the error-budget tradeoff between signal error and aliasing error.