TY  - GEN
ID  - cogprints4567
UR  - http://cogprints.org/4567/
A1  - Miravet, Carlos
A1  - Rodriguez, Francisco B.
Y1  - 2005///
N2  - 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.
PB  - Springer-Verlag
KW  - superresolution
KW  -  neural networks
KW  -  image sequence processing 
TI  - Accurate and robust image superresolution by neural processing of local image representations 
SP  - 499
AV  - public
EP  - 506
ER  -

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