Gradient Profile Prior and Its Applications in Image Super-Resolution and Enhancement

In this paper, we propose a novel generic image prior-gradient profile prior, which implies the prior knowledge of natural image gradients. In this prior, the image gradients are represented by gradient profiles, which are 1-D profiles of gradient magnitudes perpendicular to image structures. We mod...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on image processing 2011-06, Vol.20 (6), p.1529-1542
Main Authors: Sun, Jian, Xu, Zongben, Shum, Heung-Yeung
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Collection
Detection, estimation, filtering, equalization, prediction
Exact sciences and technology
Gradient field transformation
gradient profile prior
High resolution
Image edge detection
image enhancement
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Image processing
Image reconstruction
Information, signal and communications theory
Interpolation
natural image statistics
Pattern Recognition, Automated - methods
Pixel
Reproducibility of Results
Sensitivity and Specificity
Sharpness
Signal and communications theory
Signal processing
Signal, noise
Spatial resolution
State of the art
super- resolution
Telecommunications and information theory
Training
Transformations
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:In this paper, we propose a novel generic image prior-gradient profile prior, which implies the prior knowledge of natural image gradients. In this prior, the image gradients are represented by gradient profiles, which are 1-D profiles of gradient magnitudes perpendicular to image structures. We model the gradient profiles by a parametric gradient profile model. Using this model, the prior knowledge of the gradient profiles are learned from a large collection of natural images, which are called gradient profile prior. Based on this prior, we propose a gradient field transformation to constrain the gradient fields of the high resolution image and the enhanced image when performing single image super-resolution and sharpness enhancement. With this simple but very effective approach, we are able to produce state-of-the-art results. The reconstructed high resolution images or the enhanced images are sharp while have rare ringing or jaggy artifacts.
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2010.2095871