Integrated Learning-Based Super Resolution
Date Issued
2011
Date
2011
Author(s)
Chiang, Hao-Tien
Abstract
Nowadays, the requirement for image resolution increases fiercely. However, the cost of high resolution images obtained from those modern devices is usually expensive, and it is not easy for people to afford. Therefore, the techniques called “super-resolution” enhancing the low resolution image to higher one are quite important. In recent decades, many researches were dedicated in this field and plenty of algorithms were proposed.
In this thesis, we present an integrated learning-based super-resolution. Learning-based super-resolution techniques model the co-occurrence patterns between the high and low resolution patches of example images to estimate the missing details for low resolution input. Our system has two parts: training phase and synthesis phase. In the training phase, we construct a database. And in synthesis phase, we retrieve some suitable data and build multi-scale self-similarity model to update the database. We choose corresponding super-resolution algorithms based on different content, and we use back-projection to enforce global reconstruction constraint, and then enhance details of the super-resolved image.
Comparing to existing learning-based approaches, our proposed method significantly improves image quality, and the produced super-resolution images have sharp edges and rich details; moreover, the algorithm is very efficient.
Subjects
Image super-resolution
Image hallucination
Detail enhancement
Type
thesis
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