Neural Networks
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
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Extraction of high-resolution frames from video sequences
IEEE Transactions on Image Processing
Superresolution video reconstruction with arbitrary sampling lattices and nonzero aperture time
IEEE Transactions on Image Processing
A computationally efficient superresolution image reconstruction algorithm
IEEE Transactions on Image Processing
A two-step neural-network based algorithm for fast image super-resolution
Image and Vision Computing
Limits of Learning-Based Superresolution Algorithms
International Journal of Computer Vision
An adaptable k-nearest neighbors algorithm for MMSE image interpolation
IEEE Transactions on Image Processing
Invariant image reconstruction from irregular samples and hexagonal grid splines
Image and Vision Computing
Accurate and robust image superresolution by neural processing of local image representations
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
A discontinuity adaptive method for super-resolution of license plates
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
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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.