Multisensor image fusion using fast discrete curvelet transform

2007 ◽  
Author(s):  
Chengzhi Deng ◽  
Hanqiang Cao ◽  
Chao Cao ◽  
Shengqian Wang
Keyword(s):  
Image Fusion ◽  
Curvelet Transform ◽  
Fast Discrete Curvelet Transform

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

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 14898-14913 ◽  
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

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

scholarly journals An innovative image fusion algorithm based on wavelet transform and discrete fast curvelet transform

2011 ◽  
Vol 1 (3) ◽  
Author(s):  
T. Sumathi ◽  
M. Hemalatha
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Curvelet Transform ◽  
Relevant Information ◽  
Good Time ◽  
Discrete Wavelet ◽  
Fusion Algorithm ◽  
Time Frequency ◽  
Fused Image

AbstractImage fusion is the method of combining relevant information from two or more images into a single image resulting in an image that is more informative than the initial inputs. Methods for fusion include discrete wavelet transform, Laplacian pyramid based transform, curvelet based transform etc. These methods demonstrate the best performance in spatial and spectral quality of the fused image compared to other spatial methods of fusion. In particular, wavelet transform has good time-frequency characteristics. However, this characteristic cannot be extended easily to two or more dimensions with separable wavelet experiencing limited directivity when spanning a one-dimensional wavelet. This paper introduces the second generation curvelet transform and uses it to fuse images together. This method is compared against the others previously described to show that useful information can be extracted from source and fused images resulting in the production of fused images which offer clear, detailed information.

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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