A novel image de-noising method based on spherical coordinates system
Open Access
- 16 May 2012
- journal article
- Published by Springer Science and Business Media LLC in EURASIP Journal on Advances in Signal Processing
- Vol. 2012 (1), 110
- https://doi.org/10.1186/1687-6180-2012-110
Abstract
In this article, a novel image de-noising method is proposed. This method is based on spherical coordinates system. First, spherical transform is re-defined in wavelet domain, and the properties of the spherical transform in wavelet domain are listed. Then, a new adaptive threshold in spherical coordinate system is presented. It has been proved based on Besov space norm theory. After that, a novel curve shrinkage function is proposed to overcome the limitation of the traditional shrinkage functions. The new function can reach and exceed the true value and enhance the edge of the image. Finally, the multi-scale product in wavelet domain is introduced to spherical coordinates system. This article names the multi-scale product in spherical coordinates system as Multi-Scale Norm Product. The experimental results compared the improved algorithm with other methods from the peak signal-to-noise ratio, mean square error, and running time. The results indicate that improved algorithm is simple and effective.Keywords
This publication has 13 references indexed in Scilit:
- A Nonlocal SAR Image Denoising Algorithm Based on LLMMSE Wavelet ShrinkageIEEE Transactions on Geoscience and Remote Sensing, 2011
- Wavelet Thresholding-Based Denoising Method of List-Mode MLEM Algorithm for Compton ImagingIEEE Transactions on Nuclear Science, 2011
- Denoising of Hyperspectral Imagery Using Principal Component Analysis and Wavelet ShrinkageIEEE Transactions on Geoscience and Remote Sensing, 2010
- A new approach and system for attentive mobile learning based on seamless migrationApplied Intelligence, 2010
- Combining Variation and Wavelet Transform for Image ZoomingChinese Journal of Computers, 2009
- Image Denoising Using Trivariate Shrinkage Filter in the Wavelet Domain and Joint Bilateral Filter in the Spatial DomainIEEE Transactions on Image Processing, 2009
- A Discriminative Approach for Wavelet DenoisingIEEE Transactions on Image Processing, 2008
- Nonlinear wavelet image processing: variational problems, compression, and noise removal through wavelet shrinkageIEEE Transactions on Image Processing, 1998
- De-noising by soft-thresholdingIEEE Transactions on Information Theory, 1995
- Ideal spatial adaptation by wavelet shrinkageBiometrika, 1994