Technical Section: Hyper-Resolution: Image detail reconstruction through parametric edges
Computers and Graphics
A super-resolution reconstruction algorithm for surveillance images
Signal Processing
Learning-based super resolution using kernel partial least squares
Image and Vision Computing
Eigentransformation-based face super-resolution in the wavelet domain
Pattern Recognition Letters
Interactive multi-frame reconstruction for mobile devices
Multimedia Tools and Applications
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An approach to obtaining high-resolution image reconstruction from low-resolution, blurred, and noisy multiple-input frames is presented. A recursive-least-squares approach with iterative regularization is developed in the discrete Fourier transform (DFT) domain. When the input frames are processed recursively, the reconstruction does not converge in general due to the measurement noise and ill-conditioned nature of the deblurring. Through the iterative update of the regularization function and the proper choice of the regularization parameter, good high-resolution reconstructions of low-resolution, blurred, and noisy input frames are obtained. The proposed algorithm minimizes the computational requirements and provides a parallel computation structure since the reconstruction is done independently for each DFT element. Computer simulations demonstrate the performance of the algorithm