Linear inversion of ban limit reflection seismograms
SIAM Journal on Scientific and Statistical Computing
Fundamentals of digital image processing
Fundamentals of digital image processing
International Journal of Computer Vision
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
An MRF-Based Approach to Generation of Super-Resolution Images from Blurred Observations
Journal of Mathematical Imaging and Vision
Example-Based Super-Resolution
IEEE Computer Graphics and Applications
Fundamental Limits of Reconstruction-Based Superresolution Algorithms under Local Translation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Motion-Free Super-Resolution
Single-frame image super-resolution through contourlet learning
EURASIP Journal on Applied Signal Processing
A new class of two-channel biorthogonal filter banks and waveletbases
IEEE Transactions on Signal Processing
IEEE Transactions on Information Theory
Computationally attractive reconstruction of bandlimited images from irregular samples
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Imaging below the diffraction limit: a statistical analysis
IEEE Transactions on Image Processing
The contourlet transform: an efficient directional multiresolution image representation
IEEE Transactions on Image Processing
Image super-resolution based wavelet framework with gradient prior
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part II
Edge model based high resolution image generation
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
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In this paper we study the usefulness of different local and global, learning-based, single-frame image super-resolution reconstruction techniques in handling three specific tasks, namely, de-blurring, de-noising and alias removal. We start with the global, iterative Papoulis---Gerchberg method for super-resolving a scene. Next we describe a PCA-based global method which faithfully reproduces a super-resolved image from a blurred and noisy low resolution input. We also study several multi-resolution processing schemes for super-resolution where the best edges are learned locally from an image database. We show that the PCA-based global method is efficient in handling blur and noise in the data. The local methods are adept in capturing the edges properly. However, both local and global approaches cannot properly handle the aliasing present in the low resolution observation. Hence we propose an alias removal technique by designing an alias-free upsampling scheme. Here the unknown high frequency components of the given partially aliased (low resolution) image is generated by minimizing the total variation of the interpolant subject to the constraint that part of alias free spectral components in the low resolution observation are known precisely and under the assumption of sparsity in the data.