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Journal articles on the topic 'Speckle reduction'

Author: Grafiati

Published: 4 June 2021

Last updated: 12 February 2022

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1

Finn, S., M. Glavin, and E. Jones. "Echocardiographic speckle reduction comparison." IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control 58, no. 1 (January 2011): 82–101. http://dx.doi.org/10.1109/tuffc.2011.1776.

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2

Trisnadi, Jahja I. "Hadamard speckle contrast reduction." Optics Letters 29, no. 1 (January 1, 2004): 11. http://dx.doi.org/10.1364/ol.29.000011.

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3

Zakeri, F., M. R. Saradjian, and M. R. Sahebi. "SPECKLE REDUCTION IN SAR IMAGES USING A BAYESIAN MULTISCALE APPROACH." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 19, 2019): 1137–40. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-1137-2019.

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Abstract. Synthetic aperture radar (SAR) images are corrupted by speckles, which influence the interpretation of the images. Therefore, to reduce speckles and obtain reliable information from images, researchers studied different methods. This study proposes a Bayesian multiscale method, to reduce speckles in SAR images. First, it was shown that Laplacian probability density function can capture the characteristics of noise-free curvelet coefficients, and then, a maximum a posteriori (MAP) estimator was designed for estimating them. Comparison of the results obtained with those obtained from conventional speckle filters, such as Lee, Kuan, Frost, and Gamma filters, and also curvelet non-Bayesian despeckling, shows better achievement of the proposed algorithm. For instance, the improvement in different parameters is as follows: ‘noise mean value’ (NMV) 0.24 times, ‘noise standard deviation’ (NSD) 0.34 times, ‘mean square difference’ (MSD) 2.6 times and ‘equivalent number of looks’ (ENL) 0.61 times.

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4

MORI, Yutaka, and Takanori NOMURA. "Speckle Reduction in Holographic Display." Review of Laser Engineering 44, no. 7 (2016): 429. http://dx.doi.org/10.2184/lsj.44.7_429.

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5

Teo, Tat-Jin. "Speckle reduction in ultrasound imaging." Journal of the Acoustical Society of America 104, no. 3 (1998): 1156. http://dx.doi.org/10.1121/1.424317.

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6

Crimmins, Thomas R. "Geometric filter for speckle reduction." Applied Optics 24, no. 10 (May 15, 1985): 1438. http://dx.doi.org/10.1364/ao.24.001438.

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7

Karamanii, M., H. Elghandoor, and H. Ramadan. "THE DATA REDUCTION USING MATLAB FOR DIFFERENT SPECKLE IMAGES FORM SMALL SURFACES ROUGHNESS." International Journal of Advanced Research 9, no. 4 (April 30, 2021): 563–72. http://dx.doi.org/10.21474/ijar01/12732.

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The so-called laser speckles are bright spots and dark spots formed when a coherent ray is incident on a rough surface which scattered randomly in all directions, the interference of these scattered rays form these bright and dark spots (Laser Speckles).In this paper we are concerned with the formation of Objective speckles calculations. Using MATLAB the image can be converted into binary object (0 and 1) as the speckle spots intensities are dark and bright, respectively.To simplify the calculations, two processes (transform and predictive) may be used, and according to the loss of many data for the using of predictive process, the transform process is considered.The calculations are based on the evaluation on small roughness of surfaces in range 0.1 â€“ 1 Î¼m, on the same footing the contrast was considered in the range from zero to one.Fraunhofer diffraction Unfortunately, no calculations in this field had been done from other researchers.

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8

Trahey, G. E., J. W. Allison, S. W. Smith, and O. T. von Ramm. "A Quantitative Approach to Speckle Reduction via Frequency Compounding." Ultrasonic Imaging 8, no. 3 (July 1986): 151–64. http://dx.doi.org/10.1177/016173468600800301.

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Coherent speckle is a source of image noise in ultrasonic B-mode imaging. The use of multiple imaging frequencies has been suggested as a technique for speckle contrast reduction. This technique involves the averaging of images whose speckle patterns have been modified by a change in the spectrum of the transmitted or received acoustical pulse. We have measured the rate of this speckle pattern change in ultrasonic images as a function of the change in center frequency of the transmitted acoustical pulse. This data is used to quantitatively describe the trade-off of resolution loss versus speckle reduction encountered when frequency compounding is employed and to derive the optimal method of frequency compounding. These results are then used as a basis for describing the overall advisability of frequency compounding in ultrasonic imaging systems. Our analysis indicates that simple frequency compounding is counterproductive in improving image quality.

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9

Liu, Hong, Wei Sheng Wang, Bai Lin Na, and Wei Dong Liu. "Laser Speckle Reduction via Rotating Diffuser in LCOS Projection Display." Advanced Materials Research 159 (December 2010): 510–13. http://dx.doi.org/10.4028/www.scientific.net/amr.159.510.

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Through theoretical analysis the reasons of generating laser speckle, we use an optical scheme for reducing composite speckle in the three LCOS laser projection system. The project of rotating diffuser has a simple optical structure and can be easily implemented. Finally, experimental verification of rotating diffuser plate can effectively suppress laser speckle contrast.

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10

Qian Xu, Qian Xu, Zhiwei Sun Zhiwei Sun, Jianfeng Sun Jianfeng Sun, Yu Zhou Yu Zhou, Zhiyong Lu Zhiyong Lu, Xiaoping Ma Xiaoping Ma, and Liren Liu Liren Liu. "Speckle reduction of synthetic aperture imaging ladar based on wavelength characteristics." Chinese Optics Letters 12, no. 8 (2014): 080301–80305. http://dx.doi.org/10.3788/col201412.080301.

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11

MURATA, Hiroshi, Yasuyuki OKAMURA, and Kazuhisa YAMAMOTO. "Speckle Reduction Techniques in Semiconductor Lasers." Review of Laser Engineering 42, no. 7 (2014): 551. http://dx.doi.org/10.2184/lsj.42.7_551.

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12

Thomas, Weston, and Christopher Middlebrook. "Speckle Reduction in Imaging Projection Systems." Optics and Photonics Journal 02, no. 04 (2012): 338–43. http://dx.doi.org/10.4236/opj.2012.24042.

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13

Shen Tingmei, 沈婷梅, 顾瑛 Gu Ying, 王天时 Wang Tianshi, and 马国江 Ma Guojiang. "Speckle Reduction in Optical Coherence Tomography." Chinese Journal of Lasers 35, no. 9 (2008): 1437–40. http://dx.doi.org/10.3788/cjl20083509.1437.

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14

Buemi, María Elena, Alejandro C. Frery, and Heitor S. Ramos. "Speckle reduction with adaptive stack filters." Pattern Recognition Letters 36 (January 2014): 281–87. http://dx.doi.org/10.1016/j.patrec.2013.06.005.

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15

Monaghan, David, Damien Kelly, Bryan Hennelly, and Bahram Javidi. "Speckle reduction techniques in digital holography." Journal of Physics: Conference Series 206 (February 1, 2010): 012026. http://dx.doi.org/10.1088/1742-6596/206/1/012026.

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16

Seggie, D. A., and S. Leeman. "Deterministic approach towards ultrasound speckle reduction." IEE Proceedings A Physical Science, Measurement and Instrumentation, Management and Education, Reviews 134, no. 2 (1987): 188. http://dx.doi.org/10.1049/ip-a-1.1987.0026.

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17

Chaillan, Fabien, Christophe Fraschini, and Philippe Courmontagne. "Speckle noise reduction in SAS imagery." Signal Processing 87, no. 4 (April 2007): 762–81. http://dx.doi.org/10.1016/j.sigpro.2006.08.001.

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18

Gu, Zu-Han, and Anting Wang. "Speckle reduction in Collett–Wolf beams." Waves in Random and Complex Media 19, no. 3 (July 7, 2009): 535–42. http://dx.doi.org/10.1080/17455030902825091.

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19

MARTIN, F. J., and R. W. TURNER. "SAR speckle reduction by weighted filtering." International Journal of Remote Sensing 14, no. 9 (June 1993): 1759–74. http://dx.doi.org/10.1080/01431169308954000.

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20

Forsberg, F., S. Leeman, and J. A. Jensen. "Assessment of hybrid speckle reduction algorithms." Physics in Medicine and Biology 36, no. 11 (November 1, 1991): 1539–49. http://dx.doi.org/10.1088/0031-9155/36/11/013.

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21

Iwai, T., and T. Asakura. "Speckle reduction in coherent information processing." Proceedings of the IEEE 84, no. 5 (May 1996): 765–81. http://dx.doi.org/10.1109/5.488745.

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22

Borza, Dan. "OS12-1-4 Speckle Noise Reduction in Vibration Measurement by Time-average Speckle Interferometry and Digital Holography : A Unifying Approach." Abstracts of ATEM : International Conference on Advanced Technology in Experimental Mechanics : Asian Conference on Experimental Mechanics 2007.6 (2007): _OS12–1–4——_OS12–1–4—. http://dx.doi.org/10.1299/jsmeatem.2007.6._os12-1-4-.

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23

Kaur, Jaspreet, and Rajneet Kaur. "Speckle Noise Reduction in Biomedical Images Using Haar Wavelets with Wiener Filter." International Journal of Scientific Research 2, no. 5 (June 1, 2012): 120–22. http://dx.doi.org/10.15373/22778179/may2013/43.

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24

Stremplewski, Patrycjusz, Maciej Nowakowski, Dawid Borycki, and Maciej Wojtkowski. "Fast method of speckle suppression for reflection phase microscopy." Photonics Letters of Poland 10, no. 4 (December 31, 2018): 118. http://dx.doi.org/10.4302/plp.v10i4.850.

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Light propagating in turbid medium is randomly altered by optical inhomogeneities, which not only change the momentum and polarization of light but also generate a speckle pattern. All these effects strongly limit capabilities of laser based, quantitative phase–sensitive optical biomedical imaging modalities by hindering a reconstruction of phase distribution. Here we introduce the method of rapid incident light modulation, which allows to suppress speckle noise and preserve the spatial phase distribution. We implement this approach in the full-field Michelson interferometer, where the incident light is modulated using the digitalmicromirror device (DMD). Full Text: PDF ReferencesF. Zernike, "Phase contrast, a new method for the microscopic observation of transparent objects part II," Physica 9, 974-986 (1942). CrossRef M. C. Pitter, C. W. See, and M. G. Somekh, "Full-field heterodyne interference microscope with spatially incoherent illumination," Opt. Lett. 29, 1200-1202 (2004). CrossRef N. B. E. Sawyer, S. P. Morgan, M. G. Somekh, C. W. See, X. F. Cao, B. Y. Shekunov, and E. Astrakharchik, "Wide field amplitude and phase confocal microscope with parallel phase stepping," Review of Scientific Instruments 72, 3793-3801 (2001). CrossRef G. W. John and I. H. Keith, "A diffuser-based optical sectioning fluorescence microscope," Measurement Science and Technology 24, 125404 (2013). CrossRef S. Lowenthal and D. Joyeux, "Speckle Removal by a Slowly Moving Diffuser Associated with a Motionless Diffuser," J. Opt. Soc. Am. 61, 847-851 (1971). CrossRef S. Kubota and J. W. Goodman, "Very efficient speckle contrast reduction realized by moving diffuser device," Applied Optics 49, 4385-4391 (2010). CrossRef Y. Li, H. Lee, and E. Wolf, "The effect of a moving diffuser on a random electromagnetic beam," Journal of Modern Optics 52, 791-796 (2005). CrossRef C.-Y. Chen, W.-C. Su, C.-H. Lin, M.-D. Ke, Q.-L. Deng, and K.-Y. Chiu, "Reduction of speckles and distortion in projection system by using a rotating diffuser," Optical Review 19, 440-443 (2012). CrossRef J. Lehtolahti, M. Kuittinen, J. Turunen, and J. Tervo, "Coherence modulation by deterministic rotating diffusers," Opt. Express 23, 10453-10466 (2015). CrossRef J.-W. Pan and C.-H. Shih, "Speckle reduction and maintaining contrast in a LASER pico-projector using a vibrating symmetric diffuser," Opt. Express 22, 6464-6477 (2014). CrossRef J. I. Trisnadi, "Hadamard speckle contrast reduction," Optics Letters 29, 11-13 (2004). CrossRef M. Szkulmowski, I. Gorczynska, D. Szlag, M. Sylwestrzak, A. Kowalczyk, and M. Wojtkowski, "Efficient reduction of speckle noise in Optical Coherence Tomography," Opt. Express 20, 1337-1359 (2012). CrossRef J. W. Goodman, Speckle phenomena in optics: theory and applications (Roberts and Company Publishers, 2006). DirectLink Y. Choi, P. Hosseini, W. Choi, R. R. Dasari, P. T. C. So, and Z. Yaqoob, "Dynamic speckle illumination wide-field reflection phase microscopy," Opt. Lett. 39, 6062-6065 (2014). CrossRef Y. Choi, T. D. Yang, K. J. Lee, and W. Choi, "Full-field and single-shot quantitative phase microscopy using dynamic speckle illumination," Opt. Lett. 36, 2465-2467 (2011). CrossRef R. Zhou, D. Jin, P. Hosseini, V. R. Singh, Y.-h. Kim, C. Kuang, R. R. Dasari, Z. Yaqoob, and P. T. C. So, "Modeling the depth-sectioning effect in reflection-mode dynamic speckle-field interferometric microscopy," Optics Express 25, 130-143 (2017). CrossRef M. Schmitz, T. Rothe, and A. Kienle, "Evaluation of a spectrally resolved scattering microscope," Biomedical optics express 2, 2665-2678 (2011). CrossRef P. Judy, The line spread function and modulation transfer function of a computer tomography scanner, Med. Phys (1976), Vol. 3, pp. 233-236. CrossRef

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25

Hu, J., R. Guo, X. Zhu, G. Baier, and Y. Wang. "NON-LOCAL MEANS FILTER FOR POLARIMETRIC SAR SPECKLE REDUCTION-EXPERIMENTS USING TERRASAR-X DATA." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-3/W4 (March 11, 2015): 71–77. http://dx.doi.org/10.5194/isprsannals-ii-3-w4-71-2015.

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The speckle is omnipresent in synthetic aperture radar (SAR) images as an intrinsic characteristic. However, it is unwanted in certain applications. Therefore, intelligent filters for speckle reduction are of great importance. It has been demonstrated in several literatures that the non-local means filter can reduce noise while preserving details. This paper discusses non-local means filter for polarimetric SAR (PolSAR) speckle reduction. The impact of different similarity approaches, weight kernels, and parameters in the filter were analysed. A data-driven adaptive weight kernel was proposed. Combined with different similarity measures, it is compared with existing algorithms, using fully polarimetric TerraSAR-X data acquired during the commissioning phase. The proposed approach has overall the best performance in terms of speckle reduction, detail preservation, and polarimetric information preservation. This study suggests the high potential of using the developed non- local means filer for speckle reduction of PolSAR data acquired by the next generation SAR missions, e.g. TanDEM-L and TerraSAR-X NG.

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26

Rakotomamonjy, Alain, Philippe Deforge, and Pierre Marché. "Wavelet-Based Speckle Noise Reduction in Ultrasound B-Scan Images." Ultrasonic Imaging 22, no. 2 (April 2000): 73–94. http://dx.doi.org/10.1177/016173460002200201.

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Speckle noise is known to be signal-dependent in ultrasound imaging. Hence, separating noise from signal becomes a difficult task. This paper describes a wavelet-based method for reducing speckle noise. We derive from the model of the displayed ultrasound image the optimal wavelet-domain filter, in the least mean-square sense. Simulations on synthetic data have been carried out in order to assess the performance of the proposed filter with regards to the classical wavelet shrinkage scheme, while phantom and tissue images have been used for testing it on real data. The results show that the filter effectively reduces the speckle noise while preserving resolvable details.

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27

Chen, Yan, Shira L. Broschat, and Patrick J. Flynn. "Phase Insensitive Homomorphic Image Processing for Speckle Reduction." Ultrasonic Imaging 18, no. 2 (April 1996): 122–39. http://dx.doi.org/10.1177/016173469601800203.

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Speckle appears in all conventional ultrasound images and is caused by the use of a phase-sensitive transducer. Speckle is an undesirable property as it can mask small but perhaps diagnostically significant image features. In this paper a homomorphic, hybrid nonlinear processing method, based on cancellation of scattering interference, is developed and examined. Experiments with synthetic and real ultrasound imagery show that the proposed method improves the contrast-to-noise ratio in both lesion and cyst areas and preserves edge clarity.

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28

Prabakaran, K., N. Lalithamani, and P. Kiruthika. "Speckle Reduction Algorithm for Medical Ultrasound Imaging." Asian Journal of Research in Social Sciences and Humanities 6, no. 6 (2016): 148. http://dx.doi.org/10.5958/2249-7315.2016.00202.1.

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29

Li Jin-Cai, Ma Zi-Hui, Peng Yu-Xing, and Huang Bin. "Speckle reduction by image entropy anisotropic diffusion." Acta Physica Sinica 62, no. 9 (2013): 099501. http://dx.doi.org/10.7498/aps.62.099501.

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30

Mandava, Ajay K., and Emma E. Regentova. "Speckle Noise Reduction using Local Binary Pattern." Procedia Technology 6 (2012): 574–81. http://dx.doi.org/10.1016/j.protcy.2012.10.069.

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31

Li Chao, 李抄, 姜宝光 Jiang Baoguang, 夏明亮 Xia Mingliang, 程少园 Cheng Shaoyuan, and 宣丽 Xuan Li. "Laser Speckle Reduction in Retina Imaging Illumination." Acta Optica Sinica 28, no. 12 (2008): 2245–49. http://dx.doi.org/10.3788/aos20082812.2245.

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32

Zhu, Lei, Fei Gao, Weiming Wang, Qiong Wang, Jing Qin, Ying Zhao, Fangfang Zhou, Hai Zhang, and Pheng-Ann Heng. "Feature Asymmetry Anisotropic Diffusion for Speckle Reduction." Journal of Medical Imaging and Health Informatics 7, no. 1 (February 1, 2017): 197–202. http://dx.doi.org/10.1166/jmihi.2017.2006.

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33

Winetraub, Yonatan, Chris Wu, Graham P. Collins, Steven Chu, and Adam de la Zerda. "Upper limit for angular compounding speckle reduction." Applied Physics Letters 114, no. 21 (May 27, 2019): 211101. http://dx.doi.org/10.1063/1.5088709.

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34

Izquierdo, M. A. G., M. G. Hernández, J. J. Anaya, and O. Martinez. "Speckle reduction by energy time–frequency filtering." Ultrasonics 42, no. 1-9 (April 2004): 843–46. http://dx.doi.org/10.1016/j.ultras.2004.01.062.

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35

Pu, Z. B., S. Y. Ren, Z. Zhang, G. D. Liu, and Z. T. Zhuang. "Speckle Reduction Based on Ultrashort Laser Pulse." Journal of Physics: Conference Series 48 (October 1, 2006): 18–22. http://dx.doi.org/10.1088/1742-6596/48/1/004.

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36

Liu, Guojin, Xiaoping Zeng, Fengchun Tian, Zhengzhou Li, and Kadri Chaibou. "Speckle reduction by adaptive window anisotropic diffusion." Signal Processing 89, no. 11 (November 2009): 2233–43. http://dx.doi.org/10.1016/j.sigpro.2009.04.042.

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37

Dantas, Ricardo, and Eduardo Costa. "Ultrasound speckle reduction using modified gabor filters." IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control 54, no. 3 (March 2007): 530–38. http://dx.doi.org/10.1109/tuffc.2007.276.

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38

Hokland, J. H., and T. Taxt. "Ultrasound speckle reduction using harmonic oscillator models." IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control 41, no. 2 (March 1994): 215–24. http://dx.doi.org/10.1109/58.279134.

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39

Azzabou, Noura, and Nikos Paragios. "Spatio-temporal speckle reduction in ultrasound sequences." Inverse Problems & Imaging 4, no. 2 (2010): 211–22. http://dx.doi.org/10.3934/ipi.2010.4.211.

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40

Hyun, Dongwoon, Leandra L. Brickson, Kevin T. Looby, and Jeremy J. Dahl. "Beamforming and Speckle Reduction Using Neural Networks." IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control 66, no. 5 (May 2019): 898–910. http://dx.doi.org/10.1109/tuffc.2019.2903795.

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41

Harvey, E. R., and G. V. April. "Speckle reduction in synthetic-aperture-radar imagery." Optics Letters 15, no. 13 (July 1, 1990): 740. http://dx.doi.org/10.1364/ol.15.000740.

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42

Jaeger, Irina, Johan Stiens, Gaetan Koers, Gert Poesen, and Roger Vounckx. "Hadamard speckle reduction for millimeter wave imaging." Microwave and Optical Technology Letters 48, no. 9 (2006): 1722–25. http://dx.doi.org/10.1002/mop.21747.

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43

Kondo, Jun, Satoru Okagaki, Kuniko Kojima, Yuzo Nakano, and Akihisa Miyata. "Microcapsules diffuser screen for speckle noise reduction." Journal of the Society for Information Display 23, no. 12 (December 2015): 607–13. http://dx.doi.org/10.1002/jsid.402.

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44

Lee, J. S., M. R. Grunes, and S. A. Mango. "Speckle reduction in multipolarization, multifrequency SAR imagery." IEEE Transactions on Geoscience and Remote Sensing 29, no. 4 (July 1991): 535–44. http://dx.doi.org/10.1109/36.135815.

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45

Novak, L. M., and M. C. Burl. "Optimal speckle reduction in polarimetric SAR imagery." IEEE Transactions on Aerospace and Electronic Systems 26, no. 2 (March 1990): 293–305. http://dx.doi.org/10.1109/7.53442.

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46

Falldorf, Claas, Silke Huferath-von Luepke, Christoph von Kopylow, and Ralf B. Bergmann. "Reduction of speckle noise in multiwavelength contouring." Applied Optics 51, no. 34 (November 30, 2012): 8211. http://dx.doi.org/10.1364/ao.51.008211.

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47

Bansal, Er Kritika, and Er Akwinder kaur. "A Review on Speckle Noise Reduction Techniques." IOSR Journal of Computer Engineering 16, no. 3 (2014): 74–77. http://dx.doi.org/10.9790/0661-16317477.

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48

Zhang, Guo, Fengcheng Guo, Qingjun Zhang, Kai Xu, Peng Jia, and Xiaoyun Hao. "Speckle Reduction by Directional Coherent Anisotropic Diffusion." Remote Sensing 11, no. 23 (November 24, 2019): 2768. http://dx.doi.org/10.3390/rs11232768.

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To effectively balance speckle smoothing and preservation of edges and radiation, a novel anisotropic diffusion filter was developed that uses a directional coherent coefficient. The proposed filter effectively improves the edge detection operator of a traditional anisotropic diffusion filter. The new edge detection operator calculates 16 direction coherence coefficients to avoid the interference of the edge direction. For the diffusion function, the proposed method directly uses the detected directional coherent edge as the diffusion coefficient, which simplifies the calculation of the diffusion function and avoids the adverse effects of inaccurate estimation of the diffusion function threshold for a traditional anisotropic diffusion filter. The influence of the number of iterations and time steps on the proposed filter was analyzed. A series of experiments was conducted with a simulated image and three real synthetic-aperture radar images from different sensors. The results confirmed that the proposed method not only significantly reduces speckle but also effectively preserves the edge and radiation information of images.

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49

Jaybhay, Jyoti, and Rajveer Shastri. "A Study of Speckle Noise Reduction Filters." Signal & Image Processing : An International Journal 6, no. 3 (June 30, 2015): 71–80. http://dx.doi.org/10.5121/sipij.2015.6306.

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50

Guan, FaDa, Phuc Ton, ShuaiPing Ge, and LiNa Zhao. "Anisotropic diffusion filtering for ultrasound speckle reduction." Science China Technological Sciences 57, no. 3 (March 2014): 607–14. http://dx.doi.org/10.1007/s11431-014-5483-7.

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