SUPER-RESOLUTION OF HYPERSPECTRAL IMAGERY USING COMPLEX RIDGELET TRANSFORM

2012 ◽  
Vol 10 (03) ◽  
pp. 1250025 ◽  
Author(s):  
GUANGYI CHEN ◽  
SHEN-EN QIAN ◽  
JEAN-PIERRE ARDOUIN ◽  
WENFANG XIE
Keyword(s):  
Wavelet Transform ◽  
Hyperspectral Imagery ◽  
Super Resolution ◽  
Principal Component ◽  
Complex Wavelet Transform ◽  
Ridgelet Transform ◽  
Shift Invariance ◽  
Image Super Resolution ◽  
Complex Wavelet ◽  
Band Image

In this paper, a novel super-resolution method for hyperspectral imagery is proposed by using complex ridgelet transform. A Radon transform is first applied to each band image of a datacube to be enhanced to obtain the Radon slices, and then a 1D dual-tree complex wavelet transform is conducted along each Radon slice to generate coefficients of the complex ridgelet transform. The ordinary ridgelet transform or the finite ridgelet transform (FRIT), however, uses the 1D scalar wavelet transform instead of the dual-tree complex wavelet transform along each Radon slice. The reason why the dual-tree complex wavelet is adopted in this paper is because it has the property of approximate shift invariance, which is very important in image super-resolution. Experiments are conducted in this paper to demonstrate the advantages of the proposed method over the wavelet super-resolution, the FRIT image fusion, and the principal component analysis fusion.

An Image De-Noising Algorithm Based on the Dual-Tree Complex Wavelet

2011 ◽  
Vol 403-408 ◽  
pp. 1412-1415
Author(s):  
Yong Min Ning ◽  
Ling Ling Li ◽  
Hua Shi ◽  
Jing Chen
Keyword(s):  
Wavelet Transform ◽  
Invariance Property ◽  
Threshold Strategy ◽  
Complex Wavelet Transform ◽  
Shrinkage Function ◽  
Parents And Children ◽  
Shift Invariance ◽  
Complex Wavelet ◽  
Bivariate Shrinkage ◽  
Visual Results

In this paper, an image de-noising algorithm based on the Dual-Tree Complex Wavelet Transform (DT-CWT) is proposed, which takes the advantage of redundant coefficients transformed by DT-CWT. The model of bivariate shrinkage function is used to provide a nonlinear threshold strategy. It exploits the dependency between inter-scale parents and children coefficients to recover the original coefficients more accurate. With the shift-invariance property of DT-CWT coefficients, the algorithm prevents the Gibbs effect caused by the thresholding, which further improves the reconstructed quality. Experiment results show that the de-noised image using DT-CWT can achieve more than 1dB prior to DWT with impressive visual results.

scholarly journals Performance Evaluation of Quarter Shift Dual Tree Complex Wavelet Transform Based Multifocus Image Fusion Using Fusion rules

2019 ◽  
Vol 9 (4) ◽  
pp. 2377
Author(s):  
Radha N. ◽  
T.Ranga Babu
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Complex Wavelet Transform ◽  
High Quality ◽  
Method Performance ◽  
Ringing Artifacts ◽  
Fused Image ◽  
Fusion Methods ◽  
Shift Invariance ◽  
Complex Wavelet

<p>In this paper, multifocus image fusion using quarter shift dual tree complex wavelet transform is proposed. Multifocus image fusion is a technique that combines the partially focused regions of multiple images of the same scene into a fully focused fused image. Directional selectivity and shift invariance properties are essential to produce a high quality fused image. However conventional wavelet based fusion algorithms introduce the ringing artifacts into fused image due to lack of shift invariance and poor directionality. The quarter shift dual tree complex wavelet transform has proven to be an effective multi-resolution transform for image fusion with its directional and shift invariant properties. Experimentation with this transform led to the conclusion that the proposed method not only produce sharp details (focused regions) in fused image due to its good directionality but also removes artifacts with its shift invariance in order to get high quality fused image. Proposed method performance is compared with traditional fusion methods in terms of objective measures. </p>

scholarly journals A novel image fusion algorithm based on 2D scale-mixing complex wavelet transform and Bayesian MAP estimation for multimodal medical images

2017 ◽  
Vol 10 (03) ◽  
pp. 1750001 ◽  
Author(s):  
Abdallah Bengueddoudj ◽  
Zoubeida Messali ◽  
Volodymyr Mosorov
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Medical Images ◽  
Principal Component ◽  
Fusion Rule ◽  
Complex Wavelet Transform ◽  
Fusion Algorithm ◽  
Fusion Methods ◽  
Complex Wavelet ◽  
Approximation Coefficients

In this paper, we propose a new image fusion algorithm based on two-dimensional Scale-Mixing Complex Wavelet Transform (2D-SMCWT). The fusion of the detail 2D-SMCWT coefficients is performed via a Bayesian Maximum a Posteriori (MAP) approach by considering a trivariate statistical model for the local neighboring of 2D-SMCWT coefficients. For the approximation coefficients, a new fusion rule based on the Principal Component Analysis (PCA) is applied. We conduct several experiments using three different groups of multimodal medical images to evaluate the performance of the proposed method. The obtained results prove the superiority of the proposed method over the state of the art fusion methods in terms of visual quality and several commonly used metrics. Robustness of the proposed method is further tested against different types of noise. The plots of fusion metrics establish the accuracy of the proposed fusion method.

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