A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing
Remote sensing image fusion using the curvelet transform
Information Fusion
New Image Denoising Method Based Wavelet and Curvelet Transform
ICIE '09 Proceedings of the 2009 WASE International Conference on Information Engineering - Volume 01
IAS '09 Proceedings of the 2009 Fifth International Conference on Information Assurance and Security - Volume 02
Image quality based comparative evaluation of wavelet filters in ultrasound speckle reduction
Digital Signal Processing
De-noising by soft-thresholding
IEEE Transactions on Information Theory
Adaptive wavelet thresholding for image denoising and compression
IEEE Transactions on Image Processing
The curvelet transform for image denoising
IEEE Transactions on Image Processing
Gray and color image contrast enhancement by the curvelet transform
IEEE Transactions on Image Processing
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
VLSI-DSP based real time solution of DSC-SRI for an ultrasound system
Microprocessors & Microsystems
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In this paper, a novel approach is proposed which utilizes features of wavelet and curvelet transform, separately and adaptively, in 'homogeneous', 'non-homogeneous' and 'neither homogeneous nor non-homogeneous' regions, which are identified by variance approach. The edgy information that could not be retained by wavelet approach is extracted back from its residue by denoising it with curvelet transform. This extracted information is used as edge structure information (ESI) for fusing offshore regions of denoised images obtained by usage of wavelet and curvelet transform. The result of the image enhanced by such spatially adaptive fusion technique shows the improvement in the preservation of the edgy information. It also yields better smoothness in background (homogeneous region or non-edgy region) due to the removal of fuzzy edges developed during the denoising process by the curvelet transform.