Improving resolution by image registration
CVGIP: Graphical Models and Image Processing
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Super-Resolution from Image Sequences - A Review
MWSCAS '98 Proceedings of the 1998 Midwest Symposium on Systems and Circuits
A two-step neural-network based algorithm for fast image super-resolution
Image and Vision Computing
A hybrid MLP-PNN architecture for fast image superresolution
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Extraction of high-resolution frames from video sequences
IEEE Transactions on Image Processing
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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 dimensionality is firstly reduced by application of PCA. An MLP, trained on synthetic sequences with various amounts of noise, estimates the high-resolution image data. The effect of varying the dimension of the network input space is examined, showing a complex, structured behavior. Quantitative results are presented showing the accuracy and robustness of the proposed method.