Utilization of the Fast Discrete Curvelet Transform in the OFDM System

Background: Digital mammograms with appropriate image enhancement techniques will improve breast cancer detection, and thus increase the survival rates. The objectives of this study were to systematically review and compare various image enhancement techniques in digital mammograms for breast cancer detection. Methods: A literature search was conducted with the use of three online databases namely, Web of Science, Scopus, and ScienceDirect. Developed keywords strategy was used to include only the relevant articles. A Population Intervention Comparison Outcomes (PICO) strategy was used to develop the inclusion and exclusion criteria. Image quality was analyzed quantitatively based on peak signal-noise-ratio (PSNR), Mean Squared Error (MSE), Absolute Mean Brightness Error (AMBE), Entropy, and Contrast Improvement Index (CII) values. Results: Nine studies with four types of image enhancement techniques were included in this study. Two studies used histogram-based, three studies used frequency-based, one study used fuzzy-based and three studies used filter-based. All studies reported PSNR values whilst only four studies reported MSE, AMBE, Entropy and CII values. Filter-based was the highest PSNR values of 78.93, among other types. For MSE, AMBE, Entropy, and CII values, the highest were frequency-based (7.79), fuzzy-based (93.76), filter-based (7.92), and frequency-based (6.54) respectively. Conclusion: In summary, image quality for each image enhancement technique is varied, especially for breast cancer detection. In this study, the frequency-based of Fast Discrete Curvelet Transform (FDCT) via the UnequiSpaced Fast Fourier Transform (USFFT) shows the most superior among other image enhancement techniques.

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Poisson image denoising using fast discrete curvelet transform and wave atom

Signal Processing ◽

10.1016/j.sigpro.2012.01.008 ◽

2012 ◽

Vol 92 (9) ◽

pp. 2002-2017 ◽

Cited By ~ 12

Author(s):

Sandeep Palakkal ◽

K.M.M. Prabhu

Keyword(s):

Image Denoising ◽

Curvelet Transform ◽

Fast Discrete Curvelet Transform

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Noise suppression based on a fast discrete curvelet transform

Journal of Geophysics and Engineering ◽

10.1088/1742-2132/7/1/009 ◽

2010 ◽

Vol 7 (1) ◽

pp. 105-112 ◽

Cited By ~ 18

Author(s):

Zhi-yu Zhang ◽

Xiao-dan Zhang ◽

Hai-yan Yu ◽

Xue-hui Pan

Keyword(s):

Noise Suppression ◽

Curvelet Transform ◽

Fast Discrete Curvelet Transform

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Comparative analysis using fast discrete Curvelet transform via wrapping and discrete Contourlet transform for feature extraction and recognition

2013 International Conference on Intelligent Systems and Signal Processing (ISSP) ◽

10.1109/issp.2013.6526893 ◽

2013 ◽

Cited By ~ 2

Author(s):

N. G. Chitaliya ◽

A. I. Trivedi

Keyword(s):

Feature Extraction ◽

Comparative Analysis ◽

Curvelet Transform ◽

Contourlet Transform ◽

Fast Discrete Curvelet Transform

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Nonequispaced curvelet transform for seismic data reconstruction: A sparsity-promoting approach

Geophysics ◽

10.1190/1.3494032 ◽

2010 ◽

Vol 75 (6) ◽

pp. WB203-WB210 ◽

Cited By ~ 61

Author(s):

Gilles Hennenfent ◽

Lloyd Fenelon ◽

Felix J. Herrmann

Keyword(s):

Seismic Data ◽

First Generation ◽

Second Generation ◽

Synthetic Data ◽

Curvelet Transform ◽

Real Data ◽

Sampled Data ◽

Regularized Inversion ◽

Irregularly Sampled Data ◽

Fast Discrete Curvelet Transform

We extend our earlier work on the nonequispaced fast discrete curvelet transform (NFDCT) and introduce a second generation of the transform. This new generation differs from the previous one by the approach taken to compute accurate curvelet coefficients from irregularly sampled data. The first generation relies on accurate Fourier coefficients obtained by an [Formula: see text]-regularized inversion of the nonequispaced fast Fourier transform (FFT) whereas the second is based on a direct [Formula: see text]-regularized inversion of the operator that links curvelet coefficients to irregular data. Also, by construction the second generation NFDCT is lossless unlike the first generation NFDCT. This property is particularly attractive for processing irregularly sampled seismic data in the curvelet domain and bringing them back to their irregular record-ing locations with high fidelity. Secondly, we combine the second generation NFDCT with the standard fast discrete curvelet transform (FDCT) to form a new curvelet-based method, coined nonequispaced curvelet reconstruction with sparsity-promoting inversion (NCRSI) for the regularization and interpolation of irregularly sampled data. We demonstrate that for a pure regularization problem the reconstruction is very accurate. The signal-to-reconstruction error ratio in our example is above [Formula: see text]. We also conduct combined interpolation and regularization experiments. The reconstructions for synthetic data are accurate, particularly when the recording locations are optimally jittered. The reconstruction in our real data example shows amplitudes along the main wavefronts smoothly varying with limited acquisition imprint.

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A Hybrid Method for Multi-Focus Image Fusion Based on Fast Discrete Curvelet Transform

IEEE Access ◽

10.1109/access.2017.2698217 ◽

2017 ◽

Vol 5 ◽

pp. 14898-14913 ◽

Cited By ~ 16

Author(s):

Yong Yang ◽

Song Tong ◽

Shuying Huang ◽

Pan Lin ◽

Yuming Fang

Keyword(s):

Image Fusion ◽

Hybrid Method ◽

Curvelet Transform ◽

Focus Image ◽

Fast Discrete Curvelet Transform

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FDCT on the Edge of the Image Detail Enhancement Improvement

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.610.443 ◽

2014 ◽

Vol 610 ◽

pp. 443-448

Author(s):

Yong Zhang ◽

Yan Qian

Keyword(s):

Signal To Noise Ratio ◽

Curvelet Transform ◽

Objective Evaluation ◽

Threshold Method ◽

Shift Method ◽

Information Preservation ◽

Image Edge ◽

Fast Discrete Curvelet Transform ◽

Image Detail ◽

Detail Enhancement

Image edge details contains a rich amount of informations, enhancing edge details is the key of image post-processing. Traditional enhancement methods often lead to edge detail information lost. Fortunately, we find the curvelet transform good performance to reflect the detail information in the edge. In this paper, we add Wrap step to USFFT algorithm based on the Fast Discrete Curvelet Transform (FDCT), and adopt cyclic shift method and Er iteration. At the same time, we adopt adaptive threshold method. In order to get the objective evaluation result, comparing the wavelet algorithm and FDCT to the proposed method, we select peak signal-to-noise ratio. Experimental results show that the proposed method is not only superior to wavelet method, but also superior to single FDCT in the edge and detail information preservation.

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