Example Based Single-frame Image Super-resolution by Support Vector Regression
Li, Dalong; Simske, Steven
Keyword(s): Support Vector Regression, single-frame image super- resolution, ill-posed problem, example-based, machine learning
Abstract: As many other inverse problems, single-frame image super-resolution is an ill-posed problem. The problem has been approached in the context of machine learning. However, the proposed method in this paper is different from other learning based methods regarding how the input/output are formulated as well as how the learning is done. The assumption behind example based methods is the local similarity across seemingly different images. The assumption is illustrated by examples of image coding. Because of the differences in formulating the input/output and the implementation of Support Vector Regression (SVR), it is shown that the proposed approach outperforms the competing SVR method and the kernel regression method in terms of Peak Signal-to-Noise Ratio (PSNR), objective measurements of image quality. Since example based approaches are based on training, in which we know exactly what the output shall be. Therefore, it is proper to objectively measure the performance since the trained model is expected to "correctly" restore the image rather than to enhance the image, e.g. sharpening.
Additional Publication Information: To be published in Journal of Pattern Recognition Research, Vol 5, No 1 (2010).
External Posting Date: October 21, 2010 [Fulltext]. Approved for External Publication
Internal Posting Date: October 21, 2010 [Fulltext]