Image super-resolution technique mainly aims at restoring high-resolution image with satisfactory novel details. In recent years, leaning-based single-image super-resolution has been developed and proved to produce satisfactory results. With one or some dictionaries trained from a training set, learning-based super-resolution is able to establish a mapping relationship between low-resolution images and their corresponding high-resolution ones. Among all these algorithms, sparsity-based super-resolution has been proved with outstanding performance from extensive experiments. By utilizing compact dictionaries, this class of super-resolution algorithms can be efficient with lower computation complexity and has shown great potential for the practical applications.
Our proposed model, which is known as Joint Dictionary-based Super-Resolution (JDSR) algorithm, is a new sparsity-based super-resolution approach. Based on the observation that the initial values of Non-locally Centralized Sparse Representation (NCSR) model will affect the final reconstruction, we change its initial values by using results of Zeyde's model. Besides, with the purpose of further improvement, we also add a gradient histogram preservation term in the sparse model of NCSR, and modify the reference histogram estimation by a simple edge detection based enhancement so that the estimated histogram will be closer to the ground truth. The experimental results illustrate that our method outperforms the state-of-the-art methods in terms of sharper edges, clearer textures and better novel details.
| Identifer | oai:union.ndltd.org:uottawa.ca/oai:ruor.uottawa.ca:10393/34110 |
| Date | January 2016 |
| Creators | Hu, Jun |
| Contributors | Zhao, Jiying |
| Publisher | Université d'Ottawa / University of Ottawa |
| Source Sets | Université d’Ottawa |
| Language | English |
| Detected Language | English |
| Type | Thesis |
Page generated in 0.003 seconds