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Image hallucination with primal sketch priors

Jian Sun, Nan-Ning Zheng, Hai Tao, Heung-Yeung Shum·2003·2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings.

Abstract

We propose a Bayesian approach to image hallucination. Given a generic low resolution image, we hallucinate a high resolution image using a set of training images. Our work is inspired by recent progress on natural image statistics that the priors of image primitives can be well represented by examples. Specifically, primal sketch priors (e.g., edges, ridges and corners) are constructed and used to enhance the quality of the hallucinated high resolution image. Moreover, a contour smoothness constraint enforces consistency of primitives in the hallucinated image by a Markov-chain based inference algorithm. A reconstruction constraint is also applied to further improve the quality of the hallucinated image. Experiments demonstrate that our approach can hallucinate high quality super-resolution images.

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Metadata

Authors
Sun, J., Zheng, N.-N., Tao, H., & Shum, H.-Y.
Venue
2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings.
Published
2003-12-22
Volume
2
Pages
II-729-36
ISSN
1063-6919 (ISSN-L)
OpenAlex
W2114398647
Cited by
375
References
0
FWCI
6.42
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Image hallucination with primal sketch priors. https://app.cochat.ai/papers/10.1109/cvpr.2003.1211539
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Sun, J., Zheng, N.-N., Tao, H., & Shum, H.-Y. (2003). Image hallucination with primal sketch priors. 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings. https://doi.org/10.1109/cvpr.2003.1211539

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