Abstract
Image super-resolution (SR) increases the resolution of the target image, and has become a fundamental image-editing operation for real-world applications. Traditional methods often cause jaggies and blurring artifacts because natural images generally contain a lot of discrete continuities and edges. This paper proposes a new synthesis-based method for image super-resolution at a pixel level that takes advantages of convolution-based edge anti-aliasing. The target images are divided into two components representing, respectively, the high- and low-frequency contents of the images. We perform bicubic interpolation to reconstruct the missing information in the low-frequency component. A patch-based texture synthesis is subsequently adopted to synthesize the high-frequency patches with the final upscaled images. In particular, we also use the efficient edge-based anti-aliasing for correcting the quantization error, restore the high-frequency details damaged by nonlinear example-based synthesis. Our proposed approach generates super-resolution images dynamically and can be fully implemented in GPU parallelization. Experiments confirm the visual superiority of our proposed approach in comparison with competing state-of-the-art techniques. Copyright © 2015 Springer Science+Business Media New York.
Original language | English |
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Pages (from-to) | 543-560 |
Journal | Multimedia Tools and Applications |
Volume | 76 |
Issue number | 1 |
Early online date | Nov 2015 |
DOIs | |
Publication status | Published - Jan 2017 |
Citation
Jiang, X., Sheng, B., Lin, W., Li, P., Ma, L., & Shen, R. (2017). Antialiased super-resolution with parallel high-frequency synthesis. Multimedia Tools and Applications, 76(1), 543-560.Keywords
- Super-resolution
- Convolution
- Edge anti-aliasing
- Texture synthesis