Seismic Denoising with Nonuniformly Sampled Curvelets
Computing in Science and Engineering
Image denoising with complex ridgelets
Pattern Recognition
Computers in Biology and Medicine
Computing with Curvelets: From Image Processing to Turbulent Flows
Computing in Science and Engineering
Shearlet-based total variation diffusion for denoising
IEEE Transactions on Image Processing
New Method Based on Curvelet Transform for Image Denoising
ICMTMA '10 Proceedings of the 2010 International Conference on Measuring Technology and Mechatronics Automation - Volume 02
Face recognition using curvelet transform
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part I
Multipurpose Watermarking Based on Multiscale Curvelet Transform
IEEE Transactions on Information Forensics and Security
De-noising by soft-thresholding
IEEE Transactions on Information Theory
Image dependent brightness preserving histogram equalization
IEEE Transactions on Consumer Electronics
The curvelet transform for image denoising
IEEE Transactions on Image Processing
The finite ridgelet transform for image representation
IEEE Transactions on Image Processing
Gray and color image contrast enhancement by the curvelet transform
IEEE Transactions on Image Processing
Image decomposition via the combination of sparse representations and a variational approach
IEEE Transactions on Image Processing
Combined Curvelet Shrinkage and Nonlinear Anisotropic Diffusion
IEEE Transactions on Image Processing
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The conventional discrete wavelet transform (DWT) introduces artifacts during denoising of images containing smooth curves. Finite ridgelet transform (FRIT) solved this problem by mapping the curves in terms of small curved ridges. However, blind application of FRIT all over an image is computationally heavy. Finite curvelet transform (FCT) selectively applies FRIT only to the tiles containing small portions of a curve. In this work, a novel curvelet transform named as 4-quadrant finite curvelet transform (4QFCT) based on a new concept of 4-quadrant finite ridgelet transform (4QFRIT) has been proposed. An image is band pass filtered and the high frequency bands are divided into small non-overlapping square tiles. The 4QFRIT is applied to the tiles containing at least one curve element. Unlike FRIT, the 4QFRIT takes 4 sets of radon projections in all the 4 quadrants and then averages them in time and frequency domains after denoising. The proposed algorithm is extensively tested and benchmarked for denoising of images with Gaussian noise using mean squared error (MSE) and peak signal to noise ratio (PSNR). The results confirm that 4QFCT yields consistently better denoising performance quantitatively and visually.