Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Machine Learning
Example-Based Super-Resolution
IEEE Computer Graphics and Applications
Limits on Super-Resolution and How to Break Them
IEEE Transactions on Pattern Analysis and Machine Intelligence
Vector-Valued Image Regularization with PDEs: A Common Framework for Different Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Iterative Kernel Principal Component Analysis for Image Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Example-Based Learning for Single-Image Super-Resolution
Proceedings of the 30th DAGM symposium on Pattern Recognition
Image Superresolution Using Support Vector Regression
IEEE Transactions on Image Processing
Example-Based Learning for Single-Image Super-Resolution
Proceedings of the 30th DAGM symposium on Pattern Recognition
Photo zoom: high resolution from unordered image collections
Proceedings of Graphics Interface 2010
Zoom based super-resolution: a fast approach using particle swarm optimization
ICISP'10 Proceedings of the 4th international conference on Image and signal processing
ICIAR'11 Proceedings of the 8th international conference on Image analysis and recognition - Volume Part I
Improving sub-pixel correspondence through upsampling
Computer Vision and Image Understanding
Self-content super-resolution for ultra-HD up-sampling
Proceedings of the 9th European Conference on Visual Media Production
Face hallucination based on sparse local-pixel structure
Pattern Recognition
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This paper proposes a regression-based method for single-image super-resolution. Kernel ridge regression (KRR) is used to estimate the high-frequency details of the underlying high-resolution image. A sparse solution of KRR is found by combining the ideas of kernel matching pursuit and gradient descent, which allows time-complexity to be kept to a moderate level. To resolve the problem of ringing artifacts occurring due to the regularization effect, the regression results are post-processed using a prior model of a generic image class. Experimental results demonstrate the effectiveness of the proposed method.