Wavelet domain image resolution enhancement

Date

2006-02-01

Author

Temizel, Alptekin

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

142
views

0
downloads

A wavelet-domain image resolution enhancement algorithm which is based on the estimation of detail wavelet coefficients at high resolution scales is proposed. The method exploits wavelet coefficient correlation in a local neighbourhood sense and employs linear least-squares regression to estimate the unknown detail coefficients. Results show that the proposed method is considerably superior to conventional image interpolation techniques, both in objective and subjective terms, while it also compares favourably with competing methods operating in the wavelet domain.

URI

https://hdl.handle.net/11511/31868

Journal

IEE PROCEEDINGS-VISION IMAGE AND SIGNAL PROCESSING

DOI

https://doi.org/10.1049/ip-vis:20045056

Collections

Graduate School of Informatics, Article

Suggestions

OpenMETU
Core

Wavelet domain image resolution enhancement using cycle-spinning Temizel, Alptekin (2005-02-03) A wavelet domain image resolution enhancement method is proposed. The method adopts the cycle-spinning methodology adapted for use in the wavelet domain. The perceptual and objective quality of resolution enhanced images compare favourably with recently emerged algorithms in the field.
Wavelet domain image resolution enhancement using cycle spinning and edge modelling Temizel, Alptekin (2005-09-08) In this paper we present a wavelet domain image resolution en-hancement algorithm. An initial high-resolution approximation to the original image is obtained by means of zero-padding in the wavelet domain. This is further processed using the cycle-spinning methodology which reduces ringing. A critical element of the algo-rithm is the adoption of a simplified edge profile suitable for the description of edge degradations such as blurring due to loss of resolution. Linear regression using a minimal training s...
Wavelet based spectral smoothing for head-related transfer function filter design Hacıhabiboğlu, Hüseyin; Günel Kılıç, Banu (2002-01-01) Three wavelet-based spectral smoothing techniques are presented in this paper as a pre-processing stage for head-related transfer function (HRTF) filter design. These wavelet-based methods include wavelet denoising, wavelet approximation, and redundant wavelet transform. These methods are used with time-domain parametric filter design methods to reduce the order of the IIR filters which is useful for real-time implementation of immersive audio systems. Results of a subjective listening test are then present...
Image fusion for improving spatial resolution of multispectral satellite images Ünlüsoy, Deniz; Süzen, Mehmet Lütfi; Department of Geological Engineering (2013) In this study, four different image fusion techniques have been applied to high spectral and low spatial resolution satellite images with high spatial and low spectral resolution images to obtain fused images with increased spatial resolution, while preserving spectral information as much as possible. These techniques are intensity-hue-saturation (IHS) transform, principle component analysis (PCA), Brovey transform (BT), and Wavelet transform (WT) image fusion. Images used in the study belong to Çankırı reg...
3D Correspondence by Breadth First Search Frontiers Sahillioğlu, Yusuf (null; 2009-06-01) This paper presents a novel, robust, and fast 3D shape correspondence algorithm applicable to the two snapshots of the same object in arbitrary deformation. Given two such frames as triangle meshes with fixed connectivity, our algorithm first classifies vertices into Breadth-First Search (BFS) frontiers according to their unweighted shortest path distance from a source vertex. This is followed by the rigid or non-rigid alignment of the corresponding frontiers of two meshes as the second and final step. This...

Citation Formats

A. Temizel, “Wavelet domain image resolution enhancement,” IEE PROCEEDINGS-VISION IMAGE AND SIGNAL PROCESSING, pp. 25–30, 2006, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/31868.