Characterization of Signals from Multiscale Edges
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Limits on Super-Resolution and How to Break Them
IEEE Transactions on Pattern Analysis and Machine Intelligence
The steerable pyramid: a flexible architecture for multi-scale derivative computation
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 3)-Volume 3 - Volume 3
Single-frame image super-resolution through contourlet learning
EURASIP Journal on Applied Signal Processing
Image enhancement by nonlinear extrapolation in frequency space
IEEE Transactions on Image Processing
New edge-directed interpolation
IEEE Transactions on Image Processing
Edge model based high resolution image generation
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
Fast computation of edge model representation for image sequence super-resolution
PerMIn'12 Proceedings of the First Indo-Japan conference on Perception and Machine Intelligence
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Multiresolution subbands have a characteristic structure composed of sparse positive and negetive linear patterns present in close spatial proximity. This implies that there must be efficient ways to represent these subband images as compared to general images. Pixel and block transform approaches based on regular spatial sampling are unale to take this structure into account. This work introduces a novel way of representing Laplacian subbands using (oriented) edge elements. The representation is based on selecting an appropriate set of 7×7 primitives that captures the type of structures present in Laplacians. Unlike contourlets, the primitive set does not constitute a basis but has the twin advantages of small set size and close correspondence between set elements and edge elements that can be interpolated using prior models. As the chosen primitive set is not a basis, the computed representation is formulated by matching given primitives to various image regions, as opposed to decomposing given regions in terms of a basis set. This representation can be used for edge sharpness preserving magnification required in super resolution. The representation can also be exploited for lossy compression and noise removal Index terms: subband representation, primitive set, image magnification, scale-space interpolation, super-resolution.