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Journal articles on the topic 'Contourlet transform'

Author: Grafiati

Published: 4 June 2021

Last updated: 2 February 2022

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1

Hamdi, Med. "A Comparative Study in Wavelets, Curvelets and Contourlets as Denoising biomedical Images." Image Processing & Communications 16, no. 3-4 (January 1, 2011): 13–20. http://dx.doi.org/10.2478/v10248-012-0007-1.

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A Comparative Study in Wavelets, Curvelets and Contourlets as Denoising biomedical ImagesA special member of the emerging family of multi scale geometric transforms is the contourlet transform which was developed in the last few years in an attempt to overcome inherent limitations of traditional multistage representations such as curvelets and wavelets. The biomedical images were denoised using firstly wavelet than curvelets and finally contourlets transform and results are presented in this paper. It has been found that the contourlets transform outperforms the curvelets and wavelet transform in terms of signal noise ratio

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2

Han, Liang, Wen-li Zhang, Xiujuan Pu, Wanqi Cheng, and Xia Liu. "Optical nonsubsampled contourlet transform." Applied Optics 55, no. 27 (September 20, 2016): 7726. http://dx.doi.org/10.1364/ao.55.007726.

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3

Cao, Min, Shan Shan Tan, and Quan Fei Shen. "Study on Image Fusion Model Based on HIS Transform and Nonsubsampled Contourlet Transform." Key Engineering Materials 500 (January 2012): 659–65. http://dx.doi.org/10.4028/www.scientific.net/kem.500.659.

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After analysising the principle of nonsubsampled contourlet transform, the image fusion model based on HIS transform and nonsubsampled contourlet transform is proposed. By taking of ALOS image as an example, the image fusion of multi-spectral band and panchromatic band at the same time is carried out by different fusion methods such as the method combining HIS transform and nonsubsampled contourlet transform (NSCT), HIS transform fusion method, principal component analysis (PCA), Brovey and static wavelet transform (SWT). By calculating the quantitative evaluation indicators of the different fused image, it is conclued that the fusion effection of static wavelet transform fusion method and nonsubsampled contourlet transform fusion method is better than the common methods such as HIS transform, principal component analysis and Brovey. In particular, the image fusion effection of nonsubsampled contourlet transform method, which betterly maintains the image spectral information while improving image spatial resolution at the same time, is superior than the fusion evaluation of static wavelet transform fusion method.

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4

Chen, Xin Wu, and Zhan Qing Ma. "Material Texture Retrieval Using Contourlet-2.3 and Three Statistical Features." Advanced Materials Research 233-235 (May 2011): 2495–98. http://dx.doi.org/10.4028/www.scientific.net/amr.233-235.2495.

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To improve the retrieval rate of contourlet transform texture retrieval system, a contourlet-2.3 transform based retrieval system was proposed. Six different features, including mean, standard deviation, absolute mean energy, L2 energy, skewness and kurtosis contributions to retrieval rates were examined. Based on the single feature ability in retrieval system, a contourlet-2.3 retrieval system was proposed. The feature vectors were constructed by cascading the standard deviation, absolute mean energy and kurtosis of each sub-band contourlet coefficients and the similarity measure used here is Canberra distance. Experimental results on 109 brodatz texture images show that the new retrieval algorithm can lead to a higher retrieval rate than several contourlet transform retrieval systems including the original contourlet transform, non-subsampled contourlet transform under the same structure.

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Vafaie, Sepideh, and Eysa Salajegheh. "Comparisons of wavelets and contourlets for vibration-based damage identification in the plate structures." Advances in Structural Engineering 22, no. 7 (January 20, 2019): 1672–84. http://dx.doi.org/10.1177/1369433218824903.

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The objective of the present study is to compare a new approach based on contourlet transform and a traditional method based on wavelet transform to demonstrate curve damages in plate structures. The contourlet transform approach, as a novel two-dimensional development of wavelet transform, was extended to deal with inherent restrictions of wavelets. According to previous studies, wavelets have indicated poor performance to detect curve damages due to its basic elements. Therefore, this study utilized contourlet transform as a new method having an efficient performance to display this kind of discontinuities. In this research, contourlet transform and wavelet transform have been applied to a plate using four fixed boundary conditions including circle damages with arbitrary specifications and location in order to demonstrate their performance in damage identification. Comparing damage shape attained from contourlet transform and wavelet transform, it was revealed that contourlet transform could be utilized as an accessible and useful technique to detect damage with curvature.

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6

Luo, Zi Juan, and Shuai Ding. "Image Fusion Algorithm Based on Nonsubsampled Contourlet Transform." Applied Mechanics and Materials 401-403 (September 2013): 1381–84. http://dx.doi.org/10.4028/www.scientific.net/amm.401-403.1381.

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t is mostly difficult to get an image that contains all relevant objects in focus, because of the limited depth-of-focus of optical lenses. The multifocus image fusion method can solve the problem effectively. Nonsubsampled Contourlet transform has varying directions and multiple scales. When the Nonsubsampled contourlet transform is introduced to image fusion, the characteristics of original images are taken better and more information for fusion is obtained. A new method of multi-focus image fusion based on Nonsubsampled contourlet transform (NSCT) with the fusion rule of region statistics is proposed in this paper. Firstly, different focus images are decomposed using Nonsubsampled contourlet transform. Then low-bands are integrated using the weighted average, high-bands are integrated using region statistics rule. Next the fused image will be obtained by inverse Nonsubsampled contourlet transform. Finally the experimental results are showed and compared with those of method based on Contourlet transform. Experiments show that the approach can achieve better results than the method based on contourlet transform.

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7

Guo, Zhen, and Xin Wu Chen. "Contourlet-1.3 Texture Retrieval with Energy, Standard Deviation and Kurtosis." Applied Mechanics and Materials 446-447 (November 2013): 1347–52. http://dx.doi.org/10.4028/www.scientific.net/amm.446-447.1347.

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Contourlet-1.3 transform has better performance in directional information representation than the original contourlet transform due to less artifacts and local frequency characteristics, and has been studied by us in retrieval systems and has been shown it is superior to contourlet ones at retrieval rate. In order to improve the retrieval rate further, a novel contourlet-1.3 transform based texture image retrieval system was proposed in this paper. In the system, sub-bands energy, standard deviation and kurtosis in contourlet domain were cascaded to form feature vectors, and the similarity measure function was Canberra distance. Experimental results show that this contourlet-1.3 transform based image retrieval system has higher retrieval rates about 7% to that of the contourlet transform with absolute mean sub-bands energy and standard deviations features under same system structure.

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8

Li, Xiang Ying, Rui Xue, Xin Wu Chen, and Wei Luo. "Contourlet-S Retrieval Algorithm Using Absolute Mean Energy and Kurtosis Features." Applied Mechanics and Materials 197 (September 2012): 473–76. http://dx.doi.org/10.4028/www.scientific.net/amm.197.473.

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Contourlet transform has better performance in directional information representation than wavelet transform and has been studied by many researchers in retrieval systems and has been shown that it is superior to wavelet ones at retrieval rate. In order to improve the retrieval rate further, a contourlet-S transform based texture image retrieval system was proposed in this paper. In the system, the contourlet transform was constructed by non-subsampled Laplacian pyramid cascaded by critical subsampled directional filter banks, sub-bands absolute mean energy and kurtosis in contourlet-S domain are cascaded to form feature vectors, and the similarity metric is Canberra distance. Experimental results on 109 brodatz texture images show that using the features cascaded by absolute mean and kurtosis can lead to a higher retrieval rate than the combination of standard deviation and absolute mean which is most commonly used today under same dimension of feature vectors. contourlet-S transform based image retrieval system is superior to those of the original contourlet transform, non-subsampled contourlet system under the same system structure with same length of feature vectors, retrieval time and memory needed, contourlet-S decomposition structure parameters can make significant effects on retrieval rates, especially scale number.

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9

Ma, Jian Zhong, Xin Wu Chen, and Li Juan Zhong. "Contourlet-S Texture Image Retrieval System." Advanced Materials Research 433-440 (January 2012): 3408–12. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.3408.

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Contourlet transform is better in direction information representation than wavelet transform which has been studied in retrieval systems and has been shown that it is superior to wavelet ones at retrieval rate. In order to improve the retrieval rate further, an anti-aliasing contourlet-S transform based texture image retrieval system was proposed. In this system, the contourlet transform was constructed by anti-aliasing non-subsampled Laplacian pyramid cascaded by critical sub-sampled directional filter banks, sub-bands energy and standard deviations in contourlet domain are cascaded to form feature vectors, and the similarity metric used here is Canberra distance. Experimental results show that contourlet-S transform based image retrieval system is superior to those of the original contourlet transform, and non-subsampled contourlet system under the same system structure with almost same dimension of feature vectors, retrieval time and memory needed; and contourlet decomposition structure parameters can make significant effects on retrieval rates, especially scale number. To improve the retrieval rate of this system, kurtosis in each sub-band coefficients can be incorporated in features at the cost of some higher dimension of feature vectors.

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10

Liu, Yu Xi, and Xin Wu Chen. "Semi-Subsampled Contourlet Retrieval Algorithm Using Three Statistical Features." Advanced Materials Research 433-440 (January 2012): 3117–23. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.3117.

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In order to improve the retrieval rate of contourlet transform retrieval system, a semi-subsampled contourlet transform based texture image retrieval system was proposed. In the system, the contourlet transform was constructed by non-subsampled Laplacian pyramid cascaded by critical subsampled directional filter banks, sub-bands standard deviation, absolute mean energy and kurtosis in semi-subsampled contourlet domain are cascaded to form feature vectors, and the similarity metric is Canberra distance. Experimental results on 109 brodatz texture images show that using the three cascaded features can lead to a higher retrieval rate than the combination of standard deviation and absolute mean which is most commonly used today under same dimension of feature vectors. Semi-subsampled contourlet transform based image retrieval system is superior to those of the original contourlet transform, non-subsampled contourlet system under the same system structure with same length of feature vectors, retrieval time and memory needed, decomposition structure parameters can also make significant effects on retrieval rates, especially scale number.

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11

Chen, Xin Wu, Jing Ge, and Jin Gen Liu. "Non-Subsampled Contourlet Texture Retrieval Using Four Estimators." Applied Mechanics and Materials 263-266 (December 2012): 167–70. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.167.

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Contourlet transform is superior to wavelet transform in representing texture information and sparser in describing geometric structures in digital images, but lack of robust character of shift invariance. Non-subsampled contourlet transform (NSCT) alleviates this shortcoming hence more suitable for texture and has been studied for image de-noising, enhancement, and retrieval situations. Focus on improving the retrieval rates of existing contourlet transforms retrieval systems, a new texture retrieval algorithm was proposed. In the algorithm, texture information was represented by four statistical estimators, namely, L2-energy, kurtosis, standard deviation and L1-energy of each sub-band coefficients in NSCT domain. Experimental results show that the new algorithm can make a higher retrieval rate than the combination of standard deviation and energy which is most commonly used today.

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12

Chen, Xin Wu, and Li Wei Liu. "Contourlet-1.3 Texture Retrieval Using Absolute Mean Energy and Kurtosis Features." Applied Mechanics and Materials 48-49 (February 2011): 327–30. http://dx.doi.org/10.4028/www.scientific.net/amm.48-49.327.

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To improve the texture image retrieval rate of contourlet texture image retrieval system, a contourlet-1.3 transform based texture image retrieval system was proposed. In the system, the contourlet transform was contourlet-1.3, a new version of the original contourlet, sub-bands absolute mean energy and kurtosis in each contourlet-1.3 sub-band were cascaded to form feature vectors, and the similarity metric was Canberra distance. Experimental results on 109 brodatz texture images show that using the features cascaded by absolute mean energy and kurtosis can lead to a higher retrieval rate than the combination of standard deviation and absolute mean energy which is most commonly used today under same dimension of feature vectors. Contourlet-1.3 transform based image retrieval system is superior to those of the original contourlet, non-subsampled contourlet and contourlet-2.3 systems under same system structure with same dimension of feature vectors, retrieval time and memory needed.

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13

Rehman, Syed Nazeebur, and Mohameed Ali Hussain. "Glaucoma Classification Based on Contourlet Transform." Indian Journal of Public Health Research & Development 8, no. 3s (2017): 106. http://dx.doi.org/10.5958/0976-5506.2017.00251.0.

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14

Zali-Vargahan, Behrooz, Mehdi Chehel Amirani, and Hadi Seyedarabi. "Contourlet Transform for Iris Image Segmentation." International Journal of Computer Applications 60, no. 10 (December 18, 2012): 41–44. http://dx.doi.org/10.5120/9732-4209.

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15

Rodriguez-Sánchez, Rosa, J. A. García, and J. Fdez-Valdivia. "Image inpainting with nonsubsampled contourlet transform." Pattern Recognition Letters 34, no. 13 (October 2013): 1508–18. http://dx.doi.org/10.1016/j.patrec.2013.06.002.

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16

Fakhredanesh, Mohammad, Mohammad Rahmati, and Reza Safabakhsh. "Adaptive image steganography using contourlet transform." Journal of Electronic Imaging 22, no. 4 (October 16, 2013): 043007. http://dx.doi.org/10.1117/1.jei.22.4.043007.

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17

Liu, Mingna. "Image quality assessment using contourlet transform." Optical Engineering 48, no. 10 (October 1, 2009): 107201. http://dx.doi.org/10.1117/1.3241996.

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18

YAN, Chun-Man, Bao-Long GUO, and Meng YI. "Fast Algorithm for Nonsubsampled Contourlet Transform." Acta Automatica Sinica 40, no. 4 (April 2014): 757–62. http://dx.doi.org/10.1016/s1874-1029(14)60007-0.

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19

Rao, Pamarthy Chenna, and M. Ramesh Patnaik. "Contourlet Transform Based Shot Boundary Detection." International Journal of Signal Processing, Image Processing and Pattern Recognition 7, no. 4 (August 31, 2014): 381–88. http://dx.doi.org/10.14257/ijsip.2014.7.4.36.

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20

Asmare, Melkamu Hunegnaw, Vijanth S. Asirvadam, and Ahmad Fadzil M. Hani. "Image enhancement based on contourlet transform." Signal, Image and Video Processing 9, no. 7 (March 20, 2014): 1679–90. http://dx.doi.org/10.1007/s11760-014-0626-7.

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21

Yan, He, and Ying Jun Pan. "A New Non-Aliasing Nonsubsampled Contourlet Transform." Key Engineering Materials 480-481 (June 2011): 893–98. http://dx.doi.org/10.4028/www.scientific.net/kem.480-481.893.

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The factors of aliasing in the nonsubsampled Contourlet transform(NSCT) has been analyzed.The primary reason has been pointed that the à trous algorithm binary zero-interpolation brought about the width of the filter rapid increase and border distortion (that is aliasing). On that basis,a new approximate shift-invariant non-aliasing pyramidal decomposition was proposed instead of the à trous algorithm nonsubsampled pyramidal decomposition in the NSCT,So a new approximate shift-invariant non-aliasing nonsubsampled Contourlet transform(NANSCT) was constructed. Compared to the NSCT,the basis image of the NANSCT has better spatial domain regularity, frequency domain localization and decreased redundancy.The experimental results show that whether PSNR index or in visual effect, the proposed scheme outperforms the traditional Contourlet transform hard threshold denoising , Contourlet domain HMT denoising and the NSCT hard threshold denoising, and can achieve an excellent balance between suppressing noise effectively and preserving as many image details and edges as possiblet.

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22

Padma, U. R., and Jayachitra. "SELF-EMBEDDING VIDEO WATERMARKING USING DUAL ORTHOGONAL COMPLEX CONTOURLET TRANSFORM WITH AUTOCORRELATION SYSTEM." International Journal of Research -GRANTHAALAYAH 3, no. 4 (April 30, 2015): 89–98. http://dx.doi.org/10.29121/granthaalayah.v3.i4.2015.3025.

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This paper presents a novel non-blind watermarking algorithm using dual orthogonal complex contourlet transform. The dual orthogonal complex contourlet transform is preferred for watermarking because of its ability to capture the directional edges and contours superior to other transforms such as cosine transform, wavelet transform, etc. Digital image and video in their raw form require an enormous amount of storage capacity and the huge data systems also contain a lot of redundant information.Compression also increases the capacity of the communication channel. Image Compression using SPIHT Set Partitioning in Hierarchical Trees algorithm based on Huffman coding technique. SPIHT algorithm is the lossless compression algorithms reduce file size with no loss in image quality and comparing the final results in terms of bit error rate, PSNR and MSE.

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23

Chen, Xin Wu, Zhan Qing Ma, and Li Wei Liu. "Wavelet-Contourlet Retrieval Using Energy and Kurtosis Features." Advanced Materials Research 201-203 (February 2011): 2330–33. http://dx.doi.org/10.4028/www.scientific.net/amr.201-203.2330.

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To improve the retrieval rate of contourlet transform retrieval system and reduce the redundancy of contourlet which cost two much time in building feature vector database, a new wavelet-contourlet transform retrieval system was proposed. Six different features, including mean, standard deviation, absolute mean energy, L2 energy, skewness and kurotis contributions to retrieval rates were examined. Based on the single feature ability in retrieval system, a new contourlet retrieval system was proposed. The feature vectors were constructed by cascading the absolute mean energy and kurtosis of each sub-band contourlet coefficients and the similarity measure used here is Canberra distance. Experimental results on 109 brodatz texture images show that using the features cascaded by absolute mean and kurtosis can lead to a higher retrieval rate than several contourlet transform retrieval systems which utilize the combination feature of standard deviation and absolute mean energy most commonly used today under same dimension of feature vectors.

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24

Zhang, Yu Kun, Shu He, and Yong Jun Cheng. "Image Fusion Algorithm Based on Contourlet Transform." Advanced Materials Research 1044-1045 (October 2014): 1173–77. http://dx.doi.org/10.4028/www.scientific.net/amr.1044-1045.1173.

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Contourlet transform is a new multi-scale, multi-resolution analysis tool. This paper studied on the theory of Contourlet transform.,According to the practical application requirements of characteristics of data, much details in complex images it propose a novel image fusion method of remote sensing images based on contourlet Coefficients correlativity of directional region. Besides improving fused images spatial resolution, our method can better preserve original multi-spectral image’s color information.Extensive experimental results show that the proposed method is superior to conventional methods in terms of entropy, joint entropy, and average gradient. It can enhance the spatial resolution of target images. Meanwhile, it well preserves the color information of multi-spectral images.

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25

Song, Chang-Kyu, Seok-Young Kwon, and Myung-Geun Chun. "Face Recognition using Contourlet Transform and PCA." Journal of Korean Institute of Intelligent Systems 17, no. 3 (June 30, 2007): 403–9. http://dx.doi.org/10.5391/jkiis.2007.17.3.403.

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26

ZHAO, GAO-PENG, and YU-MING BO. "IMAGE FUSION ALGORITHM BASED ON NONSUBSAMPLED CONTOURLET TRANSFORM AND ESTIMATION THEORY." International Journal of Information Acquisition 06, no. 02 (June 2009): 109–16. http://dx.doi.org/10.1142/s0219878909001849.

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Aimed at the fusion of infrared and visual images, and their application demands, a new image fusion method was proposed based on the nonsubsampled contourlet transform and estimation theory. Firstly, the nonsubsampled contourlet transform was employed to decompose the source images into the low frequency subband coefficient and bandpass directional subband coefficients. Then, for the bandpass directional subband coefficients, the detail coefficients were modeled by the Gaussian mixture distributions and the EM algorithm was used in conjunction with the model to develop an iterative fusion procedure to estimate the model parameters and to produce the fused coefficients; for the fusion of the approximate subband coefficients, the rule was employed based on the energy of the pixel neighboring region. Finally, the fused image was obtained by applying the inverse nonsubsampled contourlet transform. The experimental results showed that the fusion scheme is effective and the fused image is better than that of using the wavelet transform and the contourlet transform.

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Huang, Shi Qi, Pei Feng Su, and Yi Ting Wang. "Feature Analysis and Selection of SAR Image Based on Contourlet Transform." Applied Mechanics and Materials 631-632 (September 2014): 431–35. http://dx.doi.org/10.4028/www.scientific.net/amm.631-632.431.

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Synthetic aperture radar (SAR) is a sort of microwave remote sensing imaging radar, which has much advantage. But it also has much shortcoming, such as speckle noise and directional sensitivity. Reducing impact of them to SAR image processing and applications is an important content, especially, extracting features for ground objects. Contourlet transform is a kind of multi-scale and multi-direction transform theory, and it is a sparse representation mode, too. This paper mainly studied Contourlet transform theory and its decomposition structure, and then it was used to extract SAR image features. Experimental results show that Contourlet transform can effetely extract SAR image features.

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Deng, Jun Min, Hai Yun Li, and Hao Wu. "An Approach to Lumbar Vertebra CT Image Segmentation Using Contourlet Transform and ANNs." Advanced Materials Research 468-471 (February 2012): 613–18. http://dx.doi.org/10.4028/www.scientific.net/amr.468-471.613.

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In this paper, we proposed a mathod for lumbar vertebra CT image segmentation based on the contourlet transform and artificial neural networks(ANNs). The proposed method consists of three portions. In the first part, contourlet transform is used to decompose the CT image to obtain the contourlet coefficients. In the second part, the self-organizing competitive artificial neural network is employed to optimize and extract the low frequency coefficients coefficients of contourlet transformation, reduce the number of coefficients greatly. The last part, the optimized coefficients are inverse contourlet transformed with the original coefficients,the segmented image is reconstructed. The experimental results show the accuracy of human lumbar vertebra CT image segmentation based on the proposed method is encouraged.

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29

Han, Pengcheng, and Junping Du. "Spatial Images Feature Extraction Based on Bayesian Nonlocal Means Filter and Improved Contourlet Transform." Journal of Applied Mathematics 2012 (2012): 1–16. http://dx.doi.org/10.1155/2012/467412.

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Spatial images are inevitably mixed with different levels of noise and distortion. The contourlet transform can provide multidimensional sparse representations of images in a discrete domain. Because of its filter structure, the contourlet transform is not translation-invariant. In this paper, we use a nonsubsampled pyramid structure and a nonsubsampled directional filter to achieve multidimensional and translation-invariant image decomposition for spatial images. A nonsubsampled contourlet transform is used as the basis for an improved Bayesian nonlocal means (NLM) filter for different frequencies. The Bayesian model adds a sigma range in imagea priorioperations, which can be more effective in protecting image details. The NLM filter retains the image edge content and assigns greater weight to similarities for edge pixels. Experimental results both on standard images and spatial images confirm that the proposed algorithm yields significantly better performance than nonsubsampled wavelet transform, contourlet, and curvelet approaches.

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Hui, Fan, Yong Liang Wang, and Jin Jiang Li. "Image Denoising Algorithm Based on Dyadic Contourlet Transform." Applied Mechanics and Materials 40-41 (November 2010): 591–97. http://dx.doi.org/10.4028/www.scientific.net/amm.40-41.591.

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This paper constructs a dyadic non-subsampled Contourlet transform for denoising on the image, the transformation has more directional subband, using the non-subsampled filter group for decompositing of direction, so has the translation invariance, eliminated image distortion from Contourlet transform’s lack of translation invariance. Non-subsampled filter reduces noise interference and data redundancy. Using the feature of NSCT translation invariance, multiresolution, multi-direction, and can according to the energy of NSCT in all directions and in all scale, adaptive denoising threshold. Experimental results show that compared to wavelet denoising and traditional Contourlet denoising, the method achieves a higher PSNR value, while preserving image edge details, can effectively reduce the Gibbs distortion, improve visual images.

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31

Ge, Jing, and Xin Wu Chen. "Contourlet-1.3 Texture Retrieval Algorithm by Sub-Bands Energy and Consistency." Applied Mechanics and Materials 220-223 (November 2012): 2684–87. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.2684.

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Contourlet-1.3 transform has fewer artifacts than original contourlet transform proposed by Do in 2002; it can extract image texture information more efficiently and has been studied for image de-noising, enhancement, and retrieval situations. Focus on improving the retrieval rate of contourlet-1.3 transform retrieval system, a new contourlet-1.3 texture retrieval algorithm was proposed in this paper. The feature vector of this system was a combination of sub-band energy and consistency and the similarity measure function used here was Canberra distance. Experimental results on 109 texture images coming from Brodatz album show that using the new features can make a higher retrieval rate than the combination of standard deviation and energy which is most commonly used today under the same retrieval time and system structure.

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32

ZHONG, Hua, Li-Cheng JIAO, and Peng HOU. "Retinal Vessel Segmentation Using Nonsubsampled Contourlet Transform." Chinese Journal of Computers 34, no. 3 (May 19, 2011): 574–82. http://dx.doi.org/10.3724/sp.j.1016.2011.00574.

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33

Sajedi, Hedieh. "Biometric verification by palmprint using contourlet transform." Intelligent Decision Technologies 10, no. 4 (December 8, 2016): 443–51. http://dx.doi.org/10.3233/idt-160270.

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34

Ma, Chang Xia, Ye Bi, Qi Shen Zhao, Jian Kui Shan, and Yong Zhang. "Image Edge Detection Using Nonsubsampled Contourlet Transform." Advanced Materials Research 181-182 (January 2011): 261–66. http://dx.doi.org/10.4028/www.scientific.net/amr.181-182.261.

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An algorithm for image edge detection based on nonsubsampled contourlet transform (NSCT) is proposed. NSCT is multiresolutional, localized, multidirectional and anisotropic，so it can more effectively capture high dimensional singularity. Firstly, the coefficients at different scales and in different directions are obtained by image decomposition using the NSCT, then with these coefficients thresholds are adaptively set and the generalized nonlinear gain function is used to enhance the edge features with low contrast while protecting the strong contrast features from over enhancing in the NSCT domain. The experiment results show that the algorithm achieve a good effect than other algorithms.

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35

Natarajan, V. "Blind Image Steganalysis Based on Contourlet Transform." International Journal on Cryptography and Information Security 2, no. 3 (September 30, 2012): 77–87. http://dx.doi.org/10.5121/ijcis.2012.2307.

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36

Dinesh Kumar and Vijay Kumar. "Contourlet Transform Based Watermarking for Colour Images." International journal of Multimedia & Its Applications 3, no. 1 (February 28, 2011): 122–31. http://dx.doi.org/10.5121/ijma.2011.3111.

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37

Jassim M. Abdul-Jabbar, Dr, and Hala N. Fathee. "DESIGN AND REALIZATION OF CIRCULAR CONTOURLET TRANSFORM." AL-Rafdain Engineering Journal (AREJ) 18, no. 4 (August 28, 2010): 28–42. http://dx.doi.org/10.33899/rengj.2010.31518.

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38

Al-Saif, Khalil, and Ahmed Saleh. "Denoise Digital Images Depending on Contourlet Transform." AL-Rafidain Journal of Computer Sciences and Mathematics 7, no. 3 (December 30, 2010): 227–42. http://dx.doi.org/10.33899/csmj.2010.163940.

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Feng, Peng, Yingjun Pan, Biao Wei, Wei Jin, and Deling Mi. "Enhancing retinal image by the Contourlet transform." Pattern Recognition Letters 28, no. 4 (March 2007): 516–22. http://dx.doi.org/10.1016/j.patrec.2006.09.007.

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Han, Ying-Li, and Rae-Hong Park. "Iris Recognition Using the Nonsubsampled Contourlet Transform." International Journal of Pattern Recognition and Artificial Intelligence 29, no. 08 (November 22, 2015): 1556014. http://dx.doi.org/10.1142/s0218001415560145.

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Biometric information is widely used in user identification system. Because of the unique and invariant properties of the iris through a lifetime, iris recognition is one of the most stable and reliable means in biometric identification. Extracting distinguishable iris features for iris recognition is very important. In this paper, for capturing effective texture features that represent the complex directional structures of an iris image, a new iris recognition method using the nonsubsampled contourlet transform (NSCT) features is proposed. With the shift-invariance, multiscale, and multidirection properties, significant NSCT coefficient features along the radial and angular directions in an iris image can be represented efficiently. Iris segmentation and normalization are considered at first as pre-processing. The modified normalized iris image is obtained from the normalized iris regions for extracting the robust iris features, and then is filtered with the NSCT to obtain the distinct coefficient features in each directional subband. Next, using the NSCT coefficients in each subband, an iris code vector is constructed for iris matching. Comparison of experimental results of the proposed and existing methods with three databases show the effectiveness of the proposed NSCT feature-based iris recognition algorithm, in terms of the three performance measures.

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Naimi, Amina, and Kamel Belloulata. "Multiple description image coding using contourlet transform." International Journal of Computer Aided Engineering and Technology 11, no. 1 (2019): 35. http://dx.doi.org/10.1504/ijcaet.2019.096710.

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Naimi, Amina, and Kamel Belloulata. "Multiple description image coding using contourlet transform." International Journal of Computer Aided Engineering and Technology 11, no. 1 (2019): 35. http://dx.doi.org/10.1504/ijcaet.2019.10017240.

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Sultan, Eman, Sayed El-Rabaie, Fathi E. Abd El-Samie, Nawal El-fishawy, and Said E. El-Khamy. "Digital Image Interpolation via the Contourlet Transform." International Journal of Computer Applications 43, no. 9 (April 30, 2012): 18–22. http://dx.doi.org/10.5120/6131-8364.

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H Asmare, Melkamu, Vijanth S Asirvadam, Lila Iznita, and Ahmad Fadzil M Hani. "Image Enhancement by Fusion in Contourlet Transform." International Journal on Electrical Engineering and Informatics 2, no. 1 (March 30, 2010): 29–42. http://dx.doi.org/10.15676/ijeei.2010.2.1.3.

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Wang, Haijiang, Qinke Yang, Rui Li, and Zhihong Yao. "Tunable-Q contourlet transform for image representation." Journal of Systems Engineering and Electronics 24, no. 1 (February 2013): 147–56. http://dx.doi.org/10.1109/jsee.2013.00019.

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Srivastava, R., and A. Khare. "Multifocus noisy image fusion using contourlet transform." Imaging Science Journal 63, no. 7 (July 31, 2015): 408–22. http://dx.doi.org/10.1179/1743131x15y.0000000025.

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Jiang, Gang-yi, Wen-juan Yi, Mei Yu, and Ming Yang. "Digital autofocusing method based on contourlet transform." Optoelectronics Letters 3, no. 5 (September 2007): 381–84. http://dx.doi.org/10.1007/s11801-007-6165-5.

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Fu, Kui, De Xiang Zhang, Qing Yan, and Jing Jing Zhang. "Fusion of SAR Image Using Stationary Contourlet Transform." Applied Mechanics and Materials 462-463 (November 2013): 373–77. http://dx.doi.org/10.4028/www.scientific.net/amm.462-463.373.

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The stationary contourlet transform is built upon nonsubsampled pyramids and nonsubsampled directional filter banks and provides a shift invariant directional multiresolution image representation. Firstly, several SAR images can be decomposed into low-frequency coefficients and high-frequency coefficients with multi-scales and multi-directions using the stationary contourlet transform. For the low-frequency coefficients, the average fusion method is used. For the each directional high frequency sub-band coefficients, the larger value of horizontal and vertical direction gradient information measurement is used to select the better coefficients for fusion. At last the fused image can be obtained by utilizing inverse transform for fused contourlet coefficients. Experimental results show that the proposed algorithm gives more satisfactory results than the traditional image fusion algorithms in preserving the edges and texture information.

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Chen, Xin Wu, Hua Cheng Xie, and Jin Gen Liu. "Variance Distribution and Energy Featured Non-Subsampled Contourlet Texture Retrieval." Advanced Materials Research 472-475 (February 2012): 893–96. http://dx.doi.org/10.4028/www.scientific.net/amr.472-475.893.

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Non-Subsampled contourlet transform can extract image texture information more efficiently than basic contourlet transform and has been studied for image de-noising, enhancement, and retrieval situations, its low retrieval rate are still not satisfied due to feature extraction and other reasons. Focus on improving the retrieval rate of non-subsampled contourlet transform retrieval system, a new feature named variance distribution was proposed and then a non-subsampled contourlet retrieval system was constructed in this paper. The feature vectors were constructed by cascading the energy and variance distribution of each sub-band coefficients and the similarity measure used here was Canberra distance. Experimental results show that using the new features can make a higher retrieval rate than the combination of standard deviation and energy which is most commonly used today under the same retrieval time and hardware complexity.

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Huang, Hui, Xi’an Feng, and Jionghui Jiang. "Medical Image Fusion Algorithm Based on Nonlinear Approximation of Contourlet Transform and Regional Features." Journal of Electrical and Computer Engineering 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/6807473.

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According to the pros and cons of contourlet transform and multimodality medical imaging, here we propose a novel image fusion algorithm that combines nonlinear approximation of contourlet transform with image regional features. The most important coefficient bands of the contourlet sparse matrix are retained by nonlinear approximation. Low-frequency and high-frequency regional features are also elaborated to fuse medical images. The results strongly suggested that the proposed algorithm could improve the visual effects of medical image fusion and image quality, image denoising, and enhancement.

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