Image upsampling via imposed edge statistics
ACM SIGGRAPH 2007 papers
Image Compression with Anisotropic Diffusion
Journal of Mathematical Imaging and Vision
Edge-and-corner preserving regularization for image interpolation and reconstruction
Image and Vision Computing
Example-based image super-resolution with class-specific predictors
Journal of Visual Communication and Image Representation
Reversible Interpolation of Vectorial Images by an Anisotropic Diffusion-Projection PDE
International Journal of Computer Vision
On the role of exponential splines in image interpolation
IEEE Transactions on Image Processing
VLSI implementation of an edge-oriented image scaling processor
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Vector-valued image interpolation by an anisotropic diffusion-projection PDE
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Super-resolution using sub-band constrained total variation
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
GPU-based edge-directed image interpolation
SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis
Compressive image super-resolution
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Super-resolution with sparse mixing estimators
IEEE Transactions on Image Processing
Image and video upscaling from local self-examples
ACM Transactions on Graphics (TOG)
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This paper presents a new formulation of the regularized image up-sampling problem that incorporates models of the image acquisition and display processes. We give a new analytic perspective that justifies the use of total-variation regularization from a signal processing perspective, based on an analysis that specifies the requirements of edge-directed filtering. This approach leads to a new data fidelity term that has been coupled with a total-variation regularizer to yield our objective function. This objective function is minimized using a level-sets motion that is based on the level-set method, with two types of motion that interact simultaneously. A new choice of these motions leads to a stable solution scheme that has a unique minimum. One aspect of the human visual system, perceptual uniformity, is treated in accordance with the linear nature of the data fidelity term. The method was implemented and has been verified to provide improved results, yielding crisp edges without introducing ringing or other artifacts.