NDLI: Accurate Blur Models vs. Image Priors in Single Image Super-resolution

Content Provider	IEEE Xplore Digital Library
Author	Efrat, N. Glasner, D. Apartsin, A. Nadler, B. Levin, A.
Copyright Year	2013
Description	Author affiliation: Dept. of Comput. Sci. & Appl. Math., Weizmann Inst. of Sci., Rehovot, Israel (Efrat, N.; Glasner, D.; Apartsin, A.; Nadler, B.; Levin, A.)
Abstract	Over the past decade, single image Super-Resolution (SR) research has focused on developing sophisticated image priors, leading to significant advances. Estimating and incorporating the blur model, that relates the high-res and low-res images, has received much less attention, however. In particular, the reconstruction constraint, namely that the blurred and down sampled high-res output should approximately equal the low-res input image, has been either ignored or applied with default fixed blur models. In this work, we examine the relative importance of the image prior and the reconstruction constraint. First, we show that an accurate reconstruction constraint combined with a simple gradient regularization achieves SR results almost as good as those of state-of-the-art algorithms with sophisticated image priors. Second, we study both empirically and theoretically the sensitivity of SR algorithms to the blur model assumed in the reconstruction constraint. We find that an accurate blur model is more important than a sophisticated image prior. Finally, using real camera data, we demonstrate that the default blur models of various SR algorithms may differ from the camera blur, typically leading to over-smoothed results. Our findings highlight the importance of accurately estimating camera blur in reconstructing raw lowers images acquired by an actual camera.
Sponsorship	IEEE Comput. Soc.
Starting Page	2832
Ending Page	2839
File Size	460876
Page Count	8
File Format	PDF
ISBN	9781479928408
ISSN	15505499
DOI	10.1109/ICCV.2013.352
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-01
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kernel Cameras Image reconstruction Sensitivity Algorithm design and analysis Noise Interpolation
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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