Dual-Tree Complex Wavelet Transform and Deep CNN-Based Super-Resolution for Video Inpainting with Application to Object Removal and Error Concealment

2021 ◽  
pp. 231-248
Author(s):  
Gajanan Tudavekar ◽  
Sanjay R. Patil ◽  
Santosh S. Saraf
Keyword(s):  
Wavelet Transform ◽  
Error Concealment ◽  
Super Resolution ◽  
Complex Wavelet Transform ◽  
Object Removal ◽  
Video Inpainting ◽  
Deep Cnn ◽  
Complex Wavelet

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.

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