Region-based image denoising through wavelet and fast discrete curvelet transform

2008 ◽  
Author(s):  
Yanfeng Gu ◽  
Yan Guo ◽  
Xing Liu ◽  
Ye Zhang
Keyword(s):  
Image Denoising ◽  
Curvelet Transform ◽  
Fast Discrete Curvelet Transform

Image denoising based on fast discrete curvelet transform

2009 ◽  
Vol 28 (12) ◽  
pp. 3138-3140
Author(s):  
Gao-qiu FANG ◽  
Zheng-yong WANG ◽  
Xiao-hong WU
Keyword(s):  
Image Denoising ◽  
Curvelet Transform ◽  
Fast Discrete Curvelet Transform

Medical Image Denoising With Various Noise Estimators Using Curvelet Transform

2014 ◽  
Vol 1 (2) ◽  
pp. 30-40
Author(s):  
Abha Choubey ◽  
◽  
Dr.G.R. Sinha ◽  
S. K Naik ◽  
◽  
...  
Keyword(s):  
Image Denoising ◽  
Medical Image ◽  
Curvelet Transform

Image denoising by preserving geometric components based on weighted bilateral filter and curvelet transform

Optik ◽  
2018 ◽  
Vol 159 ◽  
pp. 333-343 ◽  
Author(s):  
Sidheswar Routray ◽  
Arun Kumar Ray ◽  
Chandrabhanu Mishra
Keyword(s):  
Image Denoising ◽  
Curvelet Transform ◽  
Bilateral Filter

Digital Mammograms with Image Enhancement Techniques for Breast Cancer Detection: A Systematic Review

2021 ◽  
Vol 17 ◽  
Author(s):  
Saifullah Harith Suradi ◽  
Kamarul Amin Abdullah
Keyword(s):  
Breast Cancer ◽  
Image Quality ◽  
Image Enhancement ◽  
Cancer Detection ◽  
Mean Squared Error ◽  
Survival Rates ◽  
Curvelet Transform ◽  
Breast Cancer Detection ◽  
Online Databases ◽  
Fast Discrete Curvelet Transform

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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