On single image scale-up using sparse-representations

Authors:
Roman Zeyde;Michael Elad;Matan Protter
Affiliations:
The Computer Science Department, Technion, Israel Institute of Technology, Haifa, Israel;The Computer Science Department, Technion, Israel Institute of Technology, Haifa, Israel;The Computer Science Department, Technion, Israel Institute of Technology, Haifa, Israel
Venue:
Proceedings of the 7th international conference on Curves and Surfaces
Year:
2010

Citing 14
Cited 10

Improving resolution by image registration

CVGIP: Graphical Models and Image Processing
Learning Low-Level Vision

International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Image Denoising Via Learned Dictionaries and Sparse representation

CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Example-Based Regularization Deployed to Super-Resolution Reconstruction of a Single Image

The Computer Journal
Resolution enhancement based on learning the sparse association of image patches

Pattern Recognition Letters
From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images

SIAM Review
Image super-resolution via sparse representation

IEEE Transactions on Image Processing
Super-resolution with sparse mixing estimators

IEEE Transactions on Image Processing
Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing

Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing
Direct Sparse Deblurring

Journal of Mathematical Imaging and Vision
-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation

IEEE Transactions on Signal Processing
New edge-directed interpolation

IEEE Transactions on Image Processing
Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries

IEEE Transactions on Image Processing
Image Interpolation by Adaptive 2-D Autoregressive Modeling and Soft-Decision Estimation

IEEE Transactions on Image Processing

Image super resolution using sparse image and singular values as priors

CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part II
Enhancing face recognition at a distance using super resolution

Proceedings of the on Multimedia and security
Computer-Aided reclamation of lost art

ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
Super resolution via sparse representation in l1 framework

Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
Single image super resolution reconstruction in perturbed exemplar sub-space

ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
Low-resolution image restoration using the combination method of sparse representation and PDE model

ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories
Palmprint recognition method based on a new kernel sparse representation method

ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories and Technology
Sparse representation with morphologic regularizations for single image super-resolution

Signal Processing
Segmentation of endocardium in ultrasound images based on sparse representation over learned redundant dictionaries

Engineering Applications of Artificial Intelligence
Letters: Noise robust face hallucination employing Gaussian-Laplacian mixture model

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper deals with the single image scale-up problem using sparse-representation modeling. The goal is to recover an original image from its blurred and down-scaled noisy version. Since this problem is highly ill-posed, a prior is needed in order to regularize it. The literature offers various ways to address this problem, ranging from simple linear space-invariant interpolation schemes (e.g., bicubic interpolation), to spatially-adaptive and non-linear filters of various sorts. We embark from a recently-proposed successful algorithm by Yang et. al. [1,2], and similarly assume a local Sparse-Land model on image patches, serving as regularization. Several important modifications to the above-mentioned solution are introduced, and are shown to lead to improved results. These modifications include a major simplification of the overall process both in terms of the computational complexity and the algorithm architecture, using a different training approach for the dictionary-pair, and introducing the ability to operate without a training-set by boot-strapping the scale-up task from the given low-resolution image. We demonstrate the results on true images, showing both visual and PSNR improvements.