Image Restoration by Matching Gradient Distributions

The restoration of a blurry or noisy image is commonly performed with a MAP estimator, which maximizes a posterior probability to reconstruct a clean image from a degraded image. A MAP estimator, when used with a sparse gradient image prior, reconstructs piecewise smooth images and typically removes...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence 2012-04, Vol.34 (4), p.683-694
Main Authors: Taeg Sang Cho, Zitnick, C. L., Joshi, N., Sing Bing Kang, Szeliski, R., Freeman, W. T.
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Artificial intelligence
Computer science
control theory
systems
Cost function
Deconvolution
Exact sciences and technology
Gaussian distribution
Humans
image deblurring
image denoising
Image Enhancement - methods
image prior
Image Processing, Computer-Assisted - methods
Image reconstruction
Image restoration
Kernel
Noise
Nonblind deconvolution
Pattern recognition. Digital image processing. Computational geometry
Studies
Vision, Ocular - physiology
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The restoration of a blurry or noisy image is commonly performed with a MAP estimator, which maximizes a posterior probability to reconstruct a clean image from a degraded image. A MAP estimator, when used with a sparse gradient image prior, reconstructs piecewise smooth images and typically removes textures that are important for visual realism. We present an alternative deconvolution method called iterative distribution reweighting (IDR) which imposes a global constraint on gradients so that a reconstructed image should have a gradient distribution similar to a reference distribution. In natural images, a reference distribution not only varies from one image to another, but also within an image depending on texture. We estimate a reference distribution directly from an input image for each texture segment. Our algorithm is able to restore rich mid-frequency textures. A large-scale user study supports the conclusion that our algorithm improves the visual realism of reconstructed images compared to those of MAP estimators.
ISSN:0162-8828
1939-3539
2160-9292
DOI:10.1109/TPAMI.2011.166