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Super resolution (SR) algorithms are widely used in forensics investigations to enhance the resolution of images captured by surveillance cameras. Such algorithms usually use a common interpolation algorithm to generate an initial guess for the desired high resolution (HR) image. This initial guess is usually tuned through different methods, like learning-based or fusion-based methods, to converge...
In this paper, we consider the problem of single image super-resolution and propose a novel algorithm that outperforms state-of-the-art methods without the need of learning patches pairs from external data sets. We achieve this by modeling images and, more precisely, lines of images as piecewise smooth functions and propose a resolution enhancement method for this type of functions. The method makes...
In this paper, we propose a robust video super-resolution reconstruction method based on spatial-temporal orientation-adaptive kernel regression. First, we propose a robust registration efficiency model to reflect the temporal information reliability. Second, we propose a spatial-temporal steering kernel considering motions between frames and structures in each low resolution frame. Simulation results...
Iterative reconstruction (IR) has recently been proposed to improve multiple aspects of image quality over conventional filtered backprojection (FBP) in X-ray computed tomography (CT). FBP reconstruction and its corresponding reconstruction kernels have been optimized for decades to provide the best possible image quality. IR does not have the notion of reconstruction kernels but uses other mechanisms...
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