Increment of lateral resolution in digital holography by speckle noise removal

Optik ◽  
2010 ◽  
Vol 121 (22) ◽  
pp. 2049-2052 ◽  
Author(s):  
Freddy Alberto Monroy ◽  
Jorge Garcia-Sucerquia
Keyword(s):  
Digital Holography ◽  
Speckle Noise ◽  
Noise Removal ◽  
Lateral Resolution

scholarly journals Recent advances in speckle decorrelation modeling and processing in digital holographic interferometry

2021 ◽  
Vol 13 (4) ◽  
pp. 73
Author(s):  
Pascal Picart
Keyword(s):  
Deep Learning ◽  
Holographic Interferometry ◽  
High Speed ◽  
Digital Holography ◽  
Speckle Noise ◽  
Noise Removal ◽  
Surface Shape ◽  
Full Field ◽  
Recent Advances ◽  
Phase Data

Digital holography, and especially digital holographic interferometry, is a powerful approach for the characterization of modifications at the surface or in the volume of objects. Nevertheless, the reconstructed phase data from holographic interferometry is corrupted by the speckle noise. In this paper, we discuss on recent advances in speckle decorrelation noise removal. Two main topics are considered. The first one presents recent results in modelling the decorrelation noise in digital Fresnel holography. Especially the anisotropy of the decorrelation noise is established. The second topic presents a new approach for speckle de-noising using deep convolution neural networks. Full Text: PDF ReferencesP. Picart (ed.), New techniques in digital holography (John Wiley & Sons, 2015). CrossRef T.M. Biewer, J.C. Sawyer, C.D. Smith, C.E. Thomas, "Dual laser holography for in situ measurement of plasma facing component erosion (invited)", Rev. Sci. Instr. 89, 10J123 (2018). CrossRef M. Fratz, T. Beckmann, J. Anders, A. Bertz, M. Bayer, T. Gießler, C. Nemeth, D. Carl, "Inline application of digital holography [Invited]", Appl. Opt. 58(34), G120 (2019). CrossRef M.P. Georges, J.-F. Vandenrijt, C. Thizy, Y. Stockman, P. Queeckers, F. Dubois, D. Doyle, "Digital holographic interferometry with CO2 lasers and diffuse illumination applied to large space reflector metrology [Invited]", Appl. Opt. 52(1), A102 (2013). CrossRef E. Meteyer, F. Foucart, M. Secail-Geraud, P. Picart, C. Pezerat, "Full-field force identification with high-speed digital holography", Mech. Syst. Signal Process. 164 (2022). CrossRef L. Lagny, M. Secail-Geraud, J. Le Meur, S. Montresor, K. Heggarty, C. Pezerat, P. Picart, "Visualization of travelling waves propagating in a plate equipped with 2D ABH using wide-field holographic vibrometry", J. Sound Vib. 461 114925 (2019). CrossRef L. Valzania, Y. Zhao, L. Rong, D. Wang, M. Georges, E. Hack, P. Zolliker, "THz coherent lensless imaging", Appl. Opt. 58, G256 (2019). CrossRef V. Bianco, P. Memmolo, M. Leo, S. Montresor, C. Distante, M. Paturzo, P. Picart, B. Javidi, P. Ferraro, "Strategies for reducing speckle noise in digital holography", Light: Sci. Appl. 7(1), 1 (2018). CrossRef V. Bianco, P. Memmolo, M. Paturzo, A. Finizio, B. Javidi, P. Ferraro, "Quasi noise-free digital holography", Light. Sci. Appl. 5(9), e16142 (2016). CrossRef R. Horisaki, R. Takagi, J. Tanida, "Deep-learning-generated holography", Appl. Opt. 57(14), 3859 (2018). CrossRef E. Meteyer, F. Foucart, C. Pezerat, P. Picart, "Modeling of speckle decorrelation in digital Fresnel holographic interferometry", Opt. Expr. 29(22), 36180 (2021). CrossRef M. Piniard, B. Sorrente, G. Hug, P. Picart, "Theoretical analysis of surface-shape-induced decorrelation noise in multi-wavelength digital holography", Opt. Expr. 29(10), 14720 (2021). CrossRef P. Picart, S. Montresor, O. Sakharuk, L. Muravsky, "Refocus criterion based on maximization of the coherence factor in digital three-wavelength holographic interferometry", Opt. Lett. 42(2), 275 (2017). CrossRef P. Picart, J. Leval, "General theoretical formulation of image formation in digital Fresnel holography", J. Opt. Soc. Am. A 25, 1744 (2008). CrossRef S. Montresor, P. Picart, "Quantitative appraisal for noise reduction in digital holographic phase imaging", Opt. Expr. 24(13), 14322 (2016). CrossRef S. Montresor, M. Tahon, A. Laurent, P. Picart, "Computational de-noising based on deep learning for phase data in digital holographic interferometry", APL Photonics 5(3), 030802 (2020). CrossRef M. Tahon, S. Montresor, P. Picart, "Towards Reduced CNNs for De-Noising Phase Images Corrupted with Speckle Noise", Photonics 8(7), 255 (2021). CrossRef E. Meteyer, S. Montresor, F. Foucart, J. Le Meur, K. Heggarty, C. Pezerat, P. Picart, "Lock-in vibration retrieval based on high-speed full-field coherent imaging", Sci. Rep. 11(1), 1 (2021). CrossRef

scholarly journals MFNR: Multi-Frame Method for Complete Speckle Noise Removal in Ultrasound images Using KDE

2020 ◽  
Author(s):  
Awais Nazir ◽  
Muhammad Shahzad Younis ◽  
Muhammad Khurram Shahzad
Keyword(s):  
Signal To Noise Ratio ◽  
Low Cost ◽  
Speckle Noise ◽  
Noise Removal ◽  
General Purpose ◽  
Imaging Systems ◽  
Ultrasound Images ◽  
Frame Method ◽  
Probability Density Function Pdf ◽  
Removal System

Speckle noise is one of the most difficult noises to remove especially in medical applications. It is a nuisance in ultrasound imaging systems which is used in about half of all medical screening systems. Thus, noise removal is an important step in these systems, thereby creating reliable, automated, and potentially low cost systems. Herein, a generalized approach MFNR (Multi-Frame Noise Removal) is used, which is a complete Noise Removal system using KDE (Kernal Density Estimation). Any given type of noise can be removed if its probability density function (PDF) is known. Herein, we extracted the PDF parameters using KDE. Noise removal and detail preservation are not contrary to each other as the case in single-frame noise removal methods. Our results showed practically complete noise removal using MFNR algorithm compared to standard noise removal tools. The Peak Signal to Noise Ratio (PSNR) performance was used as a comparison metric. This paper is an extension to our previous paper where MFNR Algorithm was showed as a general purpose complete noise removal tool for all types of noises

Speckle noise removal applied to ultrasound image of carotid artery based on total least squares model

2016 ◽  
Vol 24 (5) ◽  
pp. 749-760
Author(s):  
Lei Yang ◽  
Jun Lu ◽  
Ming Dai ◽  
Li-Jie Ren ◽  
Wei-Zong Liu ◽  
...  
Keyword(s):  
Carotid Artery ◽  
Least Squares ◽  
Ultrasound Image ◽  
Speckle Noise ◽  
Noise Removal ◽  
Total Least Squares

Aberration Correction and Speckle Noise Reduction in Long Distance Digital Holography

2016 ◽  
Vol 53 (2) ◽  
pp. 020902
Author(s):  
陈波 Chen Bo ◽  
杨靖 Yang Jing ◽  
李新阳 Li Xinyang ◽  
杨旭 Yang Xu ◽  
李小阳 Li Xiaoyang
Keyword(s):  
Noise Reduction ◽  
Digital Holography ◽  
Speckle Noise ◽  
Aberration Correction ◽  
Long Distance ◽  
Speckle Noise Reduction

scholarly journals Despeckling Algorithm for Removing Speckle Noise from Ultrasound Images

Symmetry ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 938
Author(s):  
Hyunho Choi ◽  
Jechang Jeong
Keyword(s):  
Additive Noise ◽  
Low Frequency ◽  
Speckle Noise ◽  
Image Filtering ◽  
Noise Removal ◽  
Discrete Wavelet ◽  
Ultrasound Images ◽  
Noise Elimination ◽  
Residual Noise ◽  
Guided Image Filtering

Ultrasound (US) imaging can examine human bodies of various ages; however, in the process of obtaining a US image, speckle noise is generated. The speckle noise inhibits physicians from accurately examining lesions; thus, a speckle noise removal method is essential technology. To enhance speckle noise elimination, we propose a novel algorithm using the characteristics of speckle noise and filtering methods based on speckle reducing anisotropic diffusion (SRAD) filtering, discrete wavelet transform (DWT) using symmetry characteristics, weighted guided image filtering (WGIF), and gradient domain guided image filtering (GDGIF). The SRAD filter is exploited as a preprocessing filter because it can be directly applied to a medical US image containing speckle noise without a log-compression. The wavelet domain has the advantage of suppressing the additive noise. Therefore, a homomorphic transformation is utilized to convert the multiplicative noise into additive noise. After two-level DWT decomposition is applied, to suppress the residual noise of an SRAD filtered image, GDGIF and WGIF are exploited to reduce noise from seven high-frequency sub-band images and one low-frequency sub-band image, respectively. Finally, a noise-free image is attained through inverse DWT and an exponential transform. The proposed algorithm exhibits excellent speckle noise elimination and edge conservation as compared with conventional denoising methods.

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