A super-resolution reconstruction algorithm for surveillance images
Signal Processing
Super-resolution without explicit subpixel motion estimation
IEEE Transactions on Image Processing
Edge-preserving Bayesian image superresolution based on compound Markov random fields
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Joint image registration and super-resolution reconstruction based on regularized total least norm
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Efficient Fourier-wavelet super-resolution
IEEE Transactions on Image Processing - Special section on distributed camera networks: sensing, processing, communication, and implementation
Variational Bayesian image super-resolution with GPU acceleration
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
Adaptive multiple-frame image super-resolution based on U-curve
IEEE Transactions on Image Processing
Bayesian combination of sparse and non-sparse priors in image super resolution
Digital Signal Processing
Joint source and sending rate modeling in adaptive video streaming
Image Communication
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Using a stochastic framework, we propose two algorithms for the problem of obtaining a single high-resolution image from multiple noisy, blurred, and undersampled images. The first is based on a Bayesian formulation that is implemented via the expectation maximization algorithm. The second is based on a maximum a posteriori formulation. In both of our formulations, the registration, noise, and image statistics are treated as unknown parameters. These unknown parameters and the high-resolution image are estimated jointly based on the available observations. We present an efficient implementation of these algorithms in the frequency domain that allows their application to large images. Simulations are presented that test and compare the proposed algorithms.