@misc{cogprints4567,
          volume = {3696},
           title = {Accurate and robust image superresolution by neural processing of local image representations },
          author = {Carlos Miravet and Francisco B. Rodriguez},
       publisher = {Springer-Verlag},
            year = {2005},
           pages = {499--506},
        keywords = {superresolution, neural networks, image sequence processing },
             url = {http://cogprints.org/4567/},
        abstract = {Image superresolution involves the processing of an image sequence to generate a still image with higher resolution. Classical approaches, such as bayesian MAP methods, require iterative minimization procedures, with high computational costs. Recently, the authors proposed a method to tackle this problem, based on the use of a hybrid MLP-PNN architecture. In this paper, we present a novel superresolution method, based on an evolution of this concept, to incorporate the use of local image models. A neural processing stage receives as input the value of model coefficients on local windows. The data dimension-ality is firstly reduced by application of PCA. An MLP, trained on synthetic se-quences with various amounts of noise, estimates the high-resolution image data. The effect of varying the dimension of the network input space is exam-ined, showing a complex, structured behavior. Quantitative results are presented showing the accuracy and robustness of the proposed method.}
}

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