A content-aware image prior
Name
Freeman-A content-aware.pdf
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8.82 MB
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Author(s)
Cho, Taeg Sang
Joshi, Neel
Zitnick, C. Lawrence
Kang, Sing Bing
Szeliski, Richard
Freeman, William T.
Date Issued
August 2010
Journal
2010 IEEE Conference on Computer Vision and Pattern Recognition
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Cho, Taeg Sang et al. “A Content-aware Image Prior.” IEEE, 2010. 169–176. © Copyright 2010 IEEE
Version
Final published version
Abstract
n image restoration tasks, a heavy-tailed gradient distribution of natural images has been extensively exploited as an image prior. Most image restoration algorithms impose a sparse gradient prior on the whole image, reconstructing an image with piecewise smooth characteristics. While the sparse gradient prior removes ringing and noise artifacts, it also tends to remove mid-frequency textures, degrading the visual quality. We can attribute such degradations to imposing an incorrect image prior. The gradient profile in fractal-like textures, such as trees, is close to a Gaussian distribution, and small gradients from such regions are severely penalized by the sparse gradient prior. To address this issue, we introduce an image restoration algorithm that adapts the image prior to the underlying texture. We adapt the prior to both low-level local structures as well as mid-level textural characteristics. Improvements in visual quality is demonstrated on deconvolution and denoising tasks.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1109/CVPR.2010.5540214
