Single-image super-resolution using sparse regression and natural image prior

Kwang In Kim, Younghee Kwon

Research output: Contribution to journalArticlepeer-review

842 Citations (SciVal)


This paper proposes a framework for single-image super-resolution. The underlying idea is to learn a map from input low-resolution images to target high-resolution images based on example pairs of input and output images. Kernel ridge regression (KRR) is adopted for this purpose. To reduce the time complexity of training and testing for KRR, a sparse solution is found by combining the ideas of kernel matching pursuit and gradient descent. As a regularized solution, KRR leads to a better generalization than simply storing the examples as has been done in existing example-based algorithms and results in much less noisy images. However, this may introduce blurring and ringing artifacts around major edges as sharp changes are penalized severely. A prior model of a generic image class which takes into account the discontinuity property of images is adopted to resolve this problem. Comparison with existing algorithms shows the effectiveness of the proposed method.
Original languageEnglish
Pages (from-to)1127-1133
Number of pages7
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number6
Early online date22 Jan 2010
Publication statusPublished - Jun 2010


Dive into the research topics of 'Single-image super-resolution using sparse regression and natural image prior'. Together they form a unique fingerprint.

Cite this