Experimental Study on Phase Conjecture Based on Two-dimensional Image and Carrying Three-dimensional Image With Mist in Electro-holographic Reconstruction *

A method is proposed for phase conjecture based on the intensity curve of a two-dimensional(2D) image by computing a polynomial equation. The intensity values of the 2D image, which represents the distance between the image detectors and the three-dimensional(3D) scene is converted to phase information by our method. The results of numerical calculation with phase conjecture are analyzed. And what’s more, the numerical reconstruction results with phase information obtained as initial phase factors of a complex object for Fresnel kinoform and dynamic pseudorandom-phase tomographic computer holography(DPP-TCH) are compared. The peak signal-to-noise ratio(PSNR) and correlation coefficient (CC) between the reconstructed images and original object are analyzed. An experimental system is designed for photoelectric holographic reconstruction based on phase-only liquid crystal spatial light modulator(LC-SLM) and mist screen. The electro-optical experimental results indicate that suppressed the speckle noise 3D images that can be observed with naked eye have been obtained.

Synthetic Aperture Radar Image Despeckling Based on Multi-Weighted Sparse Coding

Synthetic aperture radar (SAR) images are inherently degraded by speckle noise caused by coherent imaging, which may affect the performance of the subsequent image analysis task. To resolve this problem, this article proposes an integrated SAR image despeckling model based on dictionary learning and multi-weighted sparse coding. First, the dictionary is trained by groups composed of similar image patches, which have the same structural features. An effective orthogonal dictionary with high sparse representation ability is realized by introducing a properly tight frame. Furthermore, the data-fidelity term and regularization terms are constrained by weighting factors. The weighted sparse representation model not only fully utilizes the interblock relevance but also reflects the importance of various structural groups in despeckling processing. The proposed model is implemented with fast and effective solving steps that simultaneously perform orthogonal dictionary learning, weight parameter updating, sparse coding, and image reconstruction. The solving steps are designed using the alternative minimization method. Finally, the speckles are further suppressed by iterative regularization methods. In a comparison study with existing methods, our method demonstrated state-of-the-art performance in suppressing speckle noise and protecting the image texture details.

Framelet transform and fuzzy clustering-based intelligent technique for speckle noise removal in ultrasound images

PurposeSpeckle noise removal in ultrasound images is one of the important tasks in biomedical-imaging applications. Many filtering -based despeckling methods are discussed in many existing works. Two-dimensional (2-D) transforms are also used enormously for the reduction of speckle noise in ultrasound medical images. In recent years, many soft computing-based intelligent techniques have been applied to noise removal and segmentation techniques. However, there is a requirement to improve the accuracy of despeckling using hybrid approaches.Design/methodology/approachThe work focuses on double-bank anatomy with framelet transform combined with Gaussian filter (GF) and also consists of a fuzzy kind of clustering approach for despeckling ultrasound medical images. The presented transform efficiently rejects the speckle noise based on the gray scale relative thresholding where the directional filter group (DFB) preserves the edge information.FindingsThe proposed approach is evaluated by different performance indicators such as the mean square error (MSE), peak signal to noise ratio (PSNR) speckle suppression index (SSI), mean structural similarity and the edge preservation index (EPI) accordingly. It is found that the proposed methodology is superior in terms of all the above performance indicators.Originality/valueFuzzy kind clustering methods have been proved to be better than the conventional threshold methods for noise dismissal. The algorithm gives a reconcilable development as compared to other modern speckle reduction procedures, as it preserves the geometric features even after the noise dismissal.

Improved playback uniformity of random-phase free holograms by pixel separation method

Random-phase free computer-generated holograms offer excellent quality of virtually noise-free playback of low-frequency images, but have limited efficiency in the case of highly contrast binary images with dominant high spatial frequencies. Introduction of weak random phase allows the partial suppression of this problem, but causes strong noise in the outcome. Here we present the influence of pixel separation technique on the uniformity of far field reconstructions from such random-phase free holograms. We show the improved image quality with no additional speckle noise. Full Text: PDF ReferencesJ.W. Goodman, Roberts and Company (2005). DirectLink R.W. Gerchberg, W.O. Saxton, "A practical algorithm for the determination of phase from image and diffraction plane pictures", Optik 35, 237 (1972). DirectLink M. Makowski, "Minimized speckle noise in lens-less holographic projection by pixel separation", Opt. Express 21, 29205 (2013). CrossRef I. Ducin, T. Shimobaba, M. Makowski, K. Kakarenko, A. Kowalczyk, Jaroslaw Suszek, M. Bieda, A. Kolodziejczyk, M. Sypek, "Holographic projection of images with step-less zoom and noise suppression by pixel separation", Opt. Comm. 340, 131 (2015). CrossRef T. Shimobaba, T. Ito, "Random phase-free computer-generated hologram", Opt. Express 23, 9549 (2015). CrossRef T. Shimobaba, T. Kakue, Y. Endo, R. Hirayama, D. Hiyama, S. Hasegawa, Y. Nagahama, M. Sano, M. Oikawa, T. Sugie, T. Ito, "Random phase-free kinoform for large objects", Opt. Express 23, 17269 (2015). CrossRef M. Sypek, "Light propagation in the Fresnel region. New numerical approach", Opt. Comm. 116, 43 (1995). CrossRef K. Matsushima, T. Shimobaba, "Band-Limited Angular Spectrum Method for Numerical Simulation of Free-Space Propagation in Far and Near Fields", Opt. Express 17, 19662 (2009). CrossRef

Shape and deformation measurements of rough surfaces by phase-shifting digital holography

In digital holography recording as reconstruction of holograms are performed digitally by modern photonic devices to increase of optical non-contacting measurements of various kinds of surfaces including both specular and rough surfaces. In this article we discusses these features of digital holography using phase shifting techniques that has much extended its capabilities. Full Text: PDF ReferencesG. Bruning, D.R. Herriott, J.E. Gallagher, D.P. Rosenfeld, A.D. White, D.J. Brangaccio, "Digital Wavefront Measuring Interferometer for Testing Optical Surfaces and Lenses", Appl. Opt. 13, 2693 (1974). CrossRef I. Yamaguchi, T. Zhang, "Phase-shifting digital holography", Opt. Lett. 22, 1268 (1997). CrossRef F. Zhang, I. Yamaguchi, L.P. Yaroslavsky, "Algorithm for reconstruction of digital holograms with adjustable magnification", Opt. Lett. 29, 1668 (2004). CrossRef I. Yamaguchi, "Holography, speckle, and computers", Optics and Lasers in Engineering 39, 411 (2003). CrossRef I. Yamaguchi, M. Yokota, "Speckle noise suppression in measurement by phase-shifting digital holography", Opt. Eng. 48 085602 (2009). CrossRef I. Yamaguchi, J. Kato, S. Ohta, "Surface Shape Measurement by Phase-Shifting Digital Holography", Opt. Rev. 8, 85 (2001). CrossRef I. Yamaguchi, J. Kato, H. Matsuzaki, "Measurement of surface shape and deformation by phase-shifting image digital holography", Opt. Eng. 42, 1267 (2003). CrossRef F. Zhang, J.D.R. Valera, I. Yamaguchi, M. Yokota, G. Mills, "Vibration Analysis by Phase Shifting Digital Holography", Opt. Rev. 11, 5 (2004). CrossRef

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

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

Noise Reduction, Enhancement and Classification for Sonar Images

Ultrasound imaging has some problems with image properties output. These affects the specialist decision. Ultrasound noise type is the speckle noise which has a grainy pattern depending on the signal. There are two parts of this study. The first part is the enhancing of images with adaptive Weiner, Lee, Gamma and Frost filters with 3x3, 5x5, and 7x7 sliding windows. The evaluated process was achieved using signal to noise ratio (SNR), peak signal to noise ratio (PSNR), mean square error (MSE), and maximum difference (MD) criteria. The second part consists of simulating noise in a standard image (Lina image) by adding different percentage of speckle noise from 0.01 to 0.06. The supervised classification based minimum distance method is used to evaluate the results depending on selecting four blocks located at different places on the image. Speckle noise was added with different percentage from 0.01 to 0.06 to calculate the coherent noise within the image. The coherent noise was concluded from the slope of the standard deviation with the mean for each noise. The results showed that the additive noise increased with the slide window size, while multiplicative noise did not change with the sliding window nor with increasing noise ratio. Wiener filter has the best results in enhancing the noise.