Connectionist learning procedures
Artificial Intelligence
Modular construction of time-delay neural networks for speech recognition
Neural Computation
Optimal linear combinations of neural networks
Neural Networks
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Example-Based Super-Resolution
IEEE Computer Graphics and Applications
Limits on Super-Resolution and How to Break Them
IEEE Transactions on Pattern Analysis and Machine Intelligence
A MAP Algorithm to Super-Resolution Image Reconstruction
ICIG '04 Proceedings of the Third International Conference on Image and Graphics
Adaptive mixtures of local experts
Neural Computation
A modular neural network applied to image transformation and mental images
Neural Computing and Applications
Extraction of high-resolution frames from video sequences
IEEE Transactions on Image Processing
Joint MAP registration and high-resolution image estimation using a sequence of undersampled images
IEEE Transactions on Image Processing
Image interpolation using neural networks
IEEE Transactions on Image Processing
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This paper presents the original and versatile architecture of a modular neural network and its application to super-resolution. Each module is a small multilayer perceptron, trained with the Levenberg-Marquardt method, and is used as a generic building block. By connecting the modules together to establish a composition of their individual mappings, we elaborate a lattice of modules that implements full connectivity between the pixels of the low-resolution input image and those of the higher-resolution output image. After the network is trained with patterns made up of low and high-resolution images of objects or scenes of the same kind, it will be able to enhance dramatically the resolution of a similar object's representation. The modular nature of the architecture allows the training phase to be readily parallelized on a network of PCs. Finally, it is shown that the network performs global-scale reconstruction of human faces from very low resolution input images.