Paper Info
Title
A shrinkage learning approach for single image super-resolution with overcomplete representations
Abstract
We present a novel approach for online shrinkage functions learning in single image super-resolution. The proposed approach leverages the classical Wavelet Shrinkage denoising technique where a set of scalar shrinkage functions is applied to the wavelet coefficients of a noisy image. In the proposed approach, a unique set of learned shrinkage functions is applied to the overcomplete representation coefficients of the interpolated input image. The super-resolution image is reconstructed from the post-shrinkage coefficients. During the learning stage, the lowresolution input image is treated as a reference high-resolution image and a super-resolution reconstruction process is applied to a scaled-down version of it. The shapes of all shrinkage functions are jointly learned by solving a Least Squares optimization problem that minimizes the sum of squared errors between the reference image and its super-resolution approximation. Computer simulations demonstrate superior performance compared to state-of-the-art results.
Year
DOI
Venue
2010
10.1007/978-3-642-15552-9_45
ECCV (2)
Keywords
Field
DocType
reference high-resolution image,online shrinkage function,super-resolution image,lowresolution input image,overcomplete representation,single image super-resolution,reference image,shrinkage function,interpolated input image,noisy image,super resolution,optimization problem,low resolution,sum of squares,computer simulation,least square
Noise reduction,Square (algebra),Shrinkage,Computer science,Sparse approximation,Scalar (physics),Interpolation,Artificial intelligence,Superresolution,Machine learning,Wavelet
Conference
Volume
ISSN
ISBN
6312
0302-9743
3-642-15551-0
Citations 
PageRank 
References 
15
0.92
13
Authors
3
Name
Order
Citations
PageRank
Amir Adler1968.81
Yacov Hel-Or246140.74
Michael Elad311274854.93