The Design and Use of Steerable Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
The robust estimation of multiple motions: parametric and piecewise-smooth flow fields
Computer Vision and Image Understanding
Example-Based Super-Resolution
IEEE Computer Graphics and Applications
Analysis of Persistent Motion Patterns Using the 3D Structure Tensor
WACV-MOTION '05 Proceedings of the IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2 - Volume 02
Generalizing the Nonlocal-means to super-resolution reconstruction
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Least squares and robust estimation of local image structure
Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
A new diamond search algorithm for fast block-matching motion estimation
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Fast and robust multiframe super resolution
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Statistical performance analysis of super-resolution
IEEE Transactions on Image Processing
Kernel Regression for Image Processing and Reconstruction
IEEE Transactions on Image Processing
A New SURE Approach to Image Denoising: Interscale Orthonormal Wavelet Thresholding
IEEE Transactions on Image Processing
Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering
IEEE Transactions on Image Processing
Deblurring Using Regularized Locally Adaptive Kernel Regression
IEEE Transactions on Image Processing
Super resolutionwith probabilistic motion estimation
IEEE Transactions on Image Processing
A convex approach for variational super-resolution
Proceedings of the 32nd DAGM conference on Pattern recognition
Non-local kernel regression for image and video restoration
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Reduction of JPEG compression artifacts by kernel regression and probabilistic self-organizing maps
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
Interval-valued fuzzy sets for color image super-resolution
CAEPIA'11 Proceedings of the 14th international conference on Advances in artificial intelligence: spanish association for artificial intelligence
A saliency detection model based on local and global kernel density estimation
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
Super-resolution texture synthesis using stochastic PAR/NL model
Journal of Visual Communication and Image Representation
Variational multiframe restoration of images degraded by noisy (stochastic) blur kernels
Journal of Computational and Applied Mathematics
Bayesian combination of sparse and non-sparse priors in image super resolution
Digital Signal Processing
Greedy regression in sparse coding space for single-image super-resolution
Journal of Visual Communication and Image Representation
Tetrolet regularization and learning for single frame image super-resolution
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Patch-based spatio-temporal super-resolution for video with non-rigid motion
Image Communication
Image super-resolution using local learnable kernel regression
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
Single image super-resolution based on space structure learning
Pattern Recognition Letters
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The need for precise (subpixel accuracy) motion estimates in conventional super-resolution has limited its applicability to only video sequences with relatively simple motions such as global translational or affine displacements. In this paper, we introduce a novel framework for adaptive enhancement and spatiotemporal upscaling of videos containing complex activities without explicit need for accurate motion estimation. Our approach is based on multidimensional kernel regression, where each pixel in the video sequence is approximated with a 3-D local (Taylor) series, capturing the essential local behavior of its spatiotemporal neighborhood. The coefficients of this series are estimated by solving a local weighted least-squares problem, where the weights are a function of the 3-D space-time orientation in the neighborhood. As this framework is fundamentally based upon the comparison of neighboring pixels in both space and time, it implicitly contains information about the local motion of the pixels across time, therefore rendering unnecessary an explicit computation of motions of modest size. The proposed approach not only significantly widens the applicability of super-resolution methods to a broad variety of video sequences containing complex motions, but also yields improved overall performance. Using several examples, we illustrate that the developed algorithm has super-resolution capabilities that provide improved optical resolution in the output, while being able to work on general input video with essentially arbitrary motion.