Improving resolution by image registration
CVGIP: Graphical Models and Image Processing
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Example-Based Super-Resolution
IEEE Computer Graphics and Applications
Discovering Objects and their Localization in Images
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Image upsampling via imposed edge statistics
ACM SIGGRAPH 2007 papers
Online dictionary learning for sparse coding
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Online Learning for Matrix Factorization and Sparse Coding
The Journal of Machine Learning Research
Single-Image Super-Resolution Using Sparse Regression and Natural Image Prior
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image super-resolution via sparse representation
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Sparse geometric image representations with bandelets
IEEE Transactions on Image Processing
Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
IEEE Transactions on Image Processing
Sparse Representation for Color Image Restoration
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
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We propose a new high-quality up-scaling technique that extends the existing example based super-resolution (SR) framework. Our approach is based on the fundamental idea that a low-resolution (LR) image could be generated from any of the multiple possible high-resolution (HR) images. Therefore it would be more natural to use multiple predictors of HR patch from LR patch instead of single one. In this work we build a generic framework to estimate an HR image from LR one using an adaptive prior (select the predictor locally) based on the local statistics of LR images. We use natural image patch prior as the HR image statistics. We partition the natural images into documents and group them to discover the inherent topics using probabilistic Latent Semantic Analysis (pLSA) and also learn the dual dictionaries of HR and LR image patch pairs for each of the topics using sparse dictionary learning technique. Then for test image we infer locally which topic it corresponds to and then we use the corresponding learned dual dictionary to generate HR image. Experimental results show the effectiveness of our method over existing state-of-art methods.