A hybrid MLP-PNN architecture for fast image superresolution
<|reference_start|>A hybrid MLP-PNN architecture for fast image superresolution: Image superresolution methods process an input image sequence of a scene to obtain a still image with increased resolution. Classical approaches to this problem involve complex iterative minimization procedures, typically with high computational costs. In this paper is proposed a novel algorithm for super-resolution that enables a substantial decrease in computer load. First, a probabilistic neural network architecture is used to perform a scattered-point interpolation of the image sequence data. The network kernel function is optimally determined for this problem by a multi-layer perceptron trained on synthetic data. Network parameters dependence on sequence noise level is quantitatively analyzed. This super-sampled image is spatially filtered to correct finite pixel size effects, to yield the final high-resolution estimate. Results on a real outdoor sequence are presented, showing the quality of the proposed method.<|reference_end|>
@article{miravet2005a,
    title={A hybrid MLP-PNN architecture for fast image superresolution},
    author={Carlos Miravet and Francisco B. Rodriguez (EPS-UAM, Madrid, Spain)},
    journal={Lect. Notes Comput. Sc. 2714 (2003) 401-408},
    year={2005},
    archivePrefix={arXiv},
    eprint={cs/0503053},
    primaryClass={cs.CV cs.MM}
}