Abstract

Speckle artifacts degrade image quality in virtually all modalities that utilize coherent energy, including optical coherence tomography, reflectance confocal microscopy, ultrasound, and widefield imaging with laser illumination. We present an adversarial deep learning framework for laser speckle reduction, called DeepLSR (https://durr.jhu.edu/DeepLSR), that transforms images from a source domain of coherent illumination to a target domain of speckle-free, incoherent illumination. We apply this method to widefield images of objects and tissues illuminated with a multi-wavelength laser, using light emitting diode-illuminated images as ground truth. In images of gastrointestinal tissues, DeepLSR reduces laser speckle noise by 6.4 dB, compared to a 2.9 dB reduction from optimized non-local means processing, a 3.0 dB reduction from BM3D, and a 3.7 dB reduction from an optical speckle reducer utilizing an oscillating diffuser. Further, DeepLSR can be combined with optical speckle reduction to reduce speckle noise by 9.4 dB. This dramatic reduction in speckle noise may enable the use of coherent light sources in applications that require small illumination sources and high-quality imaging, including medical endoscopy.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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References

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2018 (7)

A. A. Bindilatti, M. A. Vieira, and N. D. Mascarenhas, “Poisson wiener filtering with non-local weighted parameter estimation using stochastic distances,” Signal Process. 144, 68–76 (2018).
[Crossref]

W. Meiniel, J. Olivo-Marin, and E. D. Angelini, “Denoising of microscopy images: A review of the state-of-the-art, and a new sparsity-based method,” IEEE Transactions on Image Process. 27, 3842–3856 (2018).
[Crossref]

F. Mahmood, R. Chen, and N. J. Durr, “Unsupervised reverse domain adaptation for synthetic medical images via adversarial training,” IEEE Transactions on Med. Imaging 37, 2572–2581 (2018).
[Crossref]

F. Mahmood, R. Chen, S. Sudarsky, D. Yu, and N. J. Durr, “Deep learning with cinematic rendering: Fine-tuning deep neural networks using photorealistic medical images,” Phys. Medicine Biol. 63, 18 (2018).
[Crossref]

S. Li, M. Deng, J. Lee, A. Sinha, and G. Barbastathis, “Imaging through glass diffusers using densely connected convolutional networks,” Optica 5, 803–813 (2018).
[Crossref]

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[Crossref]

Y. Ma, X. Chen, W. Zhu, X. Cheng, D. Xiang, and F. Shi, “Speckle noise reduction in optical coherence tomography images based on edge-sensitive cGAN,” Biomed. Opt. Express 9, 5129 (2018).
[Crossref] [PubMed]

2017 (1)

O. Liba, M. D. Lew, E. D. SoRelle, R. Dutta, D. Sen, D. M. Moshfeghi, S. Chu, and A. de la Zerda, “Speckle-modulating optical coherence tomography in living mice and humans,” Nat. Commun. 8, 15845 (2017).
[Crossref] [PubMed]

2016 (2)

S. Ono and I. Yamada, “Color-line regularization for color artifact removal,” IEEE Transactions on Comput. Imaging 2, 204–217 (2016).
[Crossref]

C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE Transactions on Pattern Analysis Mach. Intell. 38, 295–307 (2016).
[Crossref]

2014 (1)

J. Salmon, Z. Harmany, C. A. Deledalle, and R. Willett, “Poisson noise reduction with non-local pca,” J. Math. Imaging Vis. 48, 279–294 (2014).
[Crossref]

2013 (3)

F. Agostinelli, M. R. Anderson, and H. Lee, “Adaptive multi-column deep neural networks with application to robust image denoising,” Proc. 26th Int. Conf. on Neural Inf. Process. Syst. - Vol.  1 pp. 1493–1501 (2013).

R. Rubinstein, T. Peleg, and M. Elad, “Analysis k-svd: A dictionary-learning algorithm for the analysis sparse model,” IEEE Transactions on Signal Process. 61, 661–677 (2013).
[Crossref]

M. R. N. Avanaki, P. P. Laissue, T. J. Eom, A. G. Podoleanu, and A. Hojjatoleslami, “Speckle reduction using an artificial neural network algorithm,” Appl. Opt. 52, 5050 (2013).
[Crossref] [PubMed]

2012 (2)

J. Schindelin, I. Arganda-Carreras, E. Frise, V. Kaynig, M. Longair, T. Pietzsch, S. Preibisch, C. Rueden, S. Saalfeld, B. Schmid, J. Y. Tinevez, D. J. White, V. Hartenstein, K. Eliceiri, P. Tomancak, and A. Cardona,“Fiji: an open-source platform for biological-image analysis,” Nat. Methods 9, 676–682 (2012).
[Crossref] [PubMed]

M. R. Christopher Glazowski, “Optimal detection pinhole for lowering speckle noise while maintaining adequate optical sectioning in confocal reflectance microscopes,” J. Biomed. Opt. 17, 085001 (2012).
[Crossref] [PubMed]

2010 (3)

2008 (1)

J. Mairal, M. Elad, and G. Sapiro, “Sparse representation for color image restoration,” IEEE Transactions on Image Process. 17, 53–69 (2008).
[Crossref]

2007 (2)

H. M. Salinas and D. C. Fernandez, “Comparison of pde-based nonlinear diffusion approaches for image enhancement and denoising in optical coherence tomography,” IEEE Transactions on Med. Imaging 26, 761–771 (2007).
[Crossref]

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-d transform-domain collaborative filtering,” IEEE Transactions on Image Process. 16, 2080–2095 (2007).
[Crossref]

2006 (1)

M. Aharon, M. Elad, and A. Bruckstein, “K-svd: An algorithm for designing overcomplete dictionaries for sparse representation,” IEEE Transactions on Signal Process. 54, 4311–4322 (2006).
[Crossref]

2004 (2)

Z. Wang, A. Bovik, H. Sheikh, and E. Simoncelli, “Image Quality Assessment: From Error Visibility to Structural Similarity,” IEEE Transactions on Image Process. 13, 600–612 (2004).
[Crossref]

D. C. Adler, T. H. Ko, and J. G. Fujimoto, “Speckle reduction in optical coherence tomography images by use of a spatially adaptive wavelet filter,” Opt. Lett. 29, 2878 (2004).
[Crossref]

2001 (1)

A. K. Dunn, H. Bolay, M. A. Moskowitz, and D. A. Boas, “Dynamic Imaging of Cerebral Blood Flow Using Laser Speckle,” J. Cereb. Blood Flow & Metab. 21, 195–201 (2001).
[Crossref]

2000 (1)

1992 (1)

L. I. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Phys. D: Nonlinear Phenom. 60, 259–268 (1992).
[Crossref]

1979 (2)

J. G. Abbott and F. Thurstone, “Acoustic speckle: Theory and experimental analysis,” Ultrason. Imaging 1, 303–324 (1979).
[Crossref] [PubMed]

T. Huang, G. Yang, and G. Tang, “A fast two-dimensional median filtering algorithm,” IEEE Transactions on Acoust. Speech, Signal Process. 27, 13–18 (1979).
[Crossref]

1976 (1)

Abbott, J. G.

J. G. Abbott and F. Thurstone, “Acoustic speckle: Theory and experimental analysis,” Ultrason. Imaging 1, 303–324 (1979).
[Crossref] [PubMed]

Abergel, R.

R. Abergel, C. Louchet, L. Moisan, and T. Zeng, “Total variation restoration of images corrupted by poisson noise with iterated conditional expectations,” in Scale Space and Variational Methods in Computer Vision, J.-F. Aujol, M. Nikolova, and N. Papadakis, eds. (Springer International Publishing, Cham, 2015), pp. 178–190.

Adler, D. C.

Agostinelli, F.

F. Agostinelli, M. R. Anderson, and H. Lee, “Adaptive multi-column deep neural networks with application to robust image denoising,” Proc. 26th Int. Conf. on Neural Inf. Process. Syst. - Vol.  1 pp. 1493–1501 (2013).

Aharon, M.

M. Aharon, M. Elad, and A. Bruckstein, “K-svd: An algorithm for designing overcomplete dictionaries for sparse representation,” IEEE Transactions on Signal Process. 54, 4311–4322 (2006).
[Crossref]

Alexander, M. B.

L. A. Gatys, M. B. Alexander, and S. Ecker, “Image style transfer using convolutional neural networks,” The IEEE Conf. on Comput. Vis. Pattern Recognit. (CVPR) (2016).

Anderson, M. R.

F. Agostinelli, M. R. Anderson, and H. Lee, “Adaptive multi-column deep neural networks with application to robust image denoising,” Proc. 26th Int. Conf. on Neural Inf. Process. Syst. - Vol.  1 pp. 1493–1501 (2013).

Angelini, E. D.

W. Meiniel, J. Olivo-Marin, and E. D. Angelini, “Denoising of microscopy images: A review of the state-of-the-art, and a new sparsity-based method,” IEEE Transactions on Image Process. 27, 3842–3856 (2018).
[Crossref]

Arganda-Carreras, I.

J. Schindelin, I. Arganda-Carreras, E. Frise, V. Kaynig, M. Longair, T. Pietzsch, S. Preibisch, C. Rueden, S. Saalfeld, B. Schmid, J. Y. Tinevez, D. J. White, V. Hartenstein, K. Eliceiri, P. Tomancak, and A. Cardona,“Fiji: an open-source platform for biological-image analysis,” Nat. Methods 9, 676–682 (2012).
[Crossref] [PubMed]

Aschwanden, M.

C. Graetzel, M. Suter, and M. Aschwanden, “Reducing laser speckle with electroactive polymer actuators,” Int. Soc. for Opt. Photonics p. 943004 (2015).

Avanaki, M. R. N.

Barbastathis, G.

Bashkansky, M.

Bindilatti, A. A.

A. A. Bindilatti, M. A. Vieira, and N. D. Mascarenhas, “Poisson wiener filtering with non-local weighted parameter estimation using stochastic distances,” Signal Process. 144, 68–76 (2018).
[Crossref]

Bioucas-Dias, J. M.

J. M. Bioucas-Dias and M. A. T. Figueiredo, “Multiplicative noise removal using variable splitting and constrained optimization,” IEEE Transactions on Image Process. 19, 1720–1730 (2010).
[Crossref]

Bizheva, K.

Boas, D. A.

A. K. Dunn, H. Bolay, M. A. Moskowitz, and D. A. Boas, “Dynamic Imaging of Cerebral Blood Flow Using Laser Speckle,” J. Cereb. Blood Flow & Metab. 21, 195–201 (2001).
[Crossref]

Bolay, H.

A. K. Dunn, H. Bolay, M. A. Moskowitz, and D. A. Boas, “Dynamic Imaging of Cerebral Blood Flow Using Laser Speckle,” J. Cereb. Blood Flow & Metab. 21, 195–201 (2001).
[Crossref]

Bovik, A.

Z. Wang, A. Bovik, H. Sheikh, and E. Simoncelli, “Image Quality Assessment: From Error Visibility to Structural Similarity,” IEEE Transactions on Image Process. 13, 600–612 (2004).
[Crossref]

Bruckstein, A.

M. Aharon, M. Elad, and A. Bruckstein, “K-svd: An algorithm for designing overcomplete dictionaries for sparse representation,” IEEE Transactions on Signal Process. 54, 4311–4322 (2006).
[Crossref]

Buades, A.

A. Buades, B. Coll, and J.-M. Morel, “A Non-Local Algorithm for Image Denoising,” 2005 IEEE Comput. Soc. Conf. on Comput. Vis. Pattern Recognit. (CVPR’05)2, 60–65.

Cardona, A.

J. Schindelin, I. Arganda-Carreras, E. Frise, V. Kaynig, M. Longair, T. Pietzsch, S. Preibisch, C. Rueden, S. Saalfeld, B. Schmid, J. Y. Tinevez, D. J. White, V. Hartenstein, K. Eliceiri, P. Tomancak, and A. Cardona,“Fiji: an open-source platform for biological-image analysis,” Nat. Methods 9, 676–682 (2012).
[Crossref] [PubMed]

Chen, R.

F. Mahmood, R. Chen, S. Sudarsky, D. Yu, and N. J. Durr, “Deep learning with cinematic rendering: Fine-tuning deep neural networks using photorealistic medical images,” Phys. Medicine Biol. 63, 18 (2018).
[Crossref]

F. Mahmood, R. Chen, and N. J. Durr, “Unsupervised reverse domain adaptation for synthetic medical images via adversarial training,” IEEE Transactions on Med. Imaging 37, 2572–2581 (2018).
[Crossref]

Chen, X.

Chen, Z.

Cheng, X.

Christopher Glazowski, M. R.

M. R. Christopher Glazowski, “Optimal detection pinhole for lowering speckle noise while maintaining adequate optical sectioning in confocal reflectance microscopes,” J. Biomed. Opt. 17, 085001 (2012).
[Crossref] [PubMed]

Chu, S.

O. Liba, M. D. Lew, E. D. SoRelle, R. Dutta, D. Sen, D. M. Moshfeghi, S. Chu, and A. de la Zerda, “Speckle-modulating optical coherence tomography in living mice and humans,” Nat. Commun. 8, 15845 (2017).
[Crossref] [PubMed]

Clausi, D. A.

Coll, B.

A. Buades, B. Coll, and J.-M. Morel, “A Non-Local Algorithm for Image Denoising,” 2005 IEEE Comput. Soc. Conf. on Comput. Vis. Pattern Recognit. (CVPR’05)2, 60–65.

Dabov, K.

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-d transform-domain collaborative filtering,” IEEE Transactions on Image Process. 16, 2080–2095 (2007).
[Crossref]

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Color image denoising via sparse 3d collaborative filtering with grouping constraint in luminance-chrominance space,” in 2007 IEEE International Conference on Image Processing, vol. 1 (2007), pp. I – 313–I – 316.

de la Zerda, A.

O. Liba, M. D. Lew, E. D. SoRelle, R. Dutta, D. Sen, D. M. Moshfeghi, S. Chu, and A. de la Zerda, “Speckle-modulating optical coherence tomography in living mice and humans,” Nat. Commun. 8, 15845 (2017).
[Crossref] [PubMed]

Deledalle, C.

C. Deledalle, F. Tupin, and L. Denis, “Poisson nl means: Unsupervised non local means for poisson noise,” in 2010 IEEE International Conference on Image Processing, (2010), pp. 801–804.
[Crossref]

Deledalle, C. A.

J. Salmon, Z. Harmany, C. A. Deledalle, and R. Willett, “Poisson noise reduction with non-local pca,” J. Math. Imaging Vis. 48, 279–294 (2014).
[Crossref]

Deng, M.

Denis, L.

C. Deledalle, F. Tupin, and L. Denis, “Poisson nl means: Unsupervised non local means for poisson noise,” in 2010 IEEE International Conference on Image Processing, (2010), pp. 801–804.
[Crossref]

Dong, C.

C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE Transactions on Pattern Analysis Mach. Intell. 38, 295–307 (2016).
[Crossref]

Dunn, A. K.

A. K. Dunn, H. Bolay, M. A. Moskowitz, and D. A. Boas, “Dynamic Imaging of Cerebral Blood Flow Using Laser Speckle,” J. Cereb. Blood Flow & Metab. 21, 195–201 (2001).
[Crossref]

Durr, N. J.

F. Mahmood, R. Chen, S. Sudarsky, D. Yu, and N. J. Durr, “Deep learning with cinematic rendering: Fine-tuning deep neural networks using photorealistic medical images,” Phys. Medicine Biol. 63, 18 (2018).
[Crossref]

F. Mahmood, R. Chen, and N. J. Durr, “Unsupervised reverse domain adaptation for synthetic medical images via adversarial training,” IEEE Transactions on Med. Imaging 37, 2572–2581 (2018).
[Crossref]

Dutta, R.

O. Liba, M. D. Lew, E. D. SoRelle, R. Dutta, D. Sen, D. M. Moshfeghi, S. Chu, and A. de la Zerda, “Speckle-modulating optical coherence tomography in living mice and humans,” Nat. Commun. 8, 15845 (2017).
[Crossref] [PubMed]

Ecker, S.

L. A. Gatys, M. B. Alexander, and S. Ecker, “Image style transfer using convolutional neural networks,” The IEEE Conf. on Comput. Vis. Pattern Recognit. (CVPR) (2016).

Egiazarian, K.

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-d transform-domain collaborative filtering,” IEEE Transactions on Image Process. 16, 2080–2095 (2007).
[Crossref]

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Color image denoising via sparse 3d collaborative filtering with grouping constraint in luminance-chrominance space,” in 2007 IEEE International Conference on Image Processing, vol. 1 (2007), pp. I – 313–I – 316.

Elad, M.

R. Rubinstein, T. Peleg, and M. Elad, “Analysis k-svd: A dictionary-learning algorithm for the analysis sparse model,” IEEE Transactions on Signal Process. 61, 661–677 (2013).
[Crossref]

J. Mairal, M. Elad, and G. Sapiro, “Sparse representation for color image restoration,” IEEE Transactions on Image Process. 17, 53–69 (2008).
[Crossref]

M. Aharon, M. Elad, and A. Bruckstein, “K-svd: An algorithm for designing overcomplete dictionaries for sparse representation,” IEEE Transactions on Signal Process. 54, 4311–4322 (2006).
[Crossref]

Eliceiri, K.

J. Schindelin, I. Arganda-Carreras, E. Frise, V. Kaynig, M. Longair, T. Pietzsch, S. Preibisch, C. Rueden, S. Saalfeld, B. Schmid, J. Y. Tinevez, D. J. White, V. Hartenstein, K. Eliceiri, P. Tomancak, and A. Cardona,“Fiji: an open-source platform for biological-image analysis,” Nat. Methods 9, 676–682 (2012).
[Crossref] [PubMed]

Eom, T. J.

Fatemi, E.

L. I. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Phys. D: Nonlinear Phenom. 60, 259–268 (1992).
[Crossref]

Fernandez, D. C.

H. M. Salinas and D. C. Fernandez, “Comparison of pde-based nonlinear diffusion approaches for image enhancement and denoising in optical coherence tomography,” IEEE Transactions on Med. Imaging 26, 761–771 (2007).
[Crossref]

Figueiredo, M. A. T.

J. M. Bioucas-Dias and M. A. T. Figueiredo, “Multiplicative noise removal using variable splitting and constrained optimization,” IEEE Transactions on Image Process. 19, 1720–1730 (2010).
[Crossref]

Foi, A.

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-d transform-domain collaborative filtering,” IEEE Transactions on Image Process. 16, 2080–2095 (2007).
[Crossref]

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Color image denoising via sparse 3d collaborative filtering with grouping constraint in luminance-chrominance space,” in 2007 IEEE International Conference on Image Processing, vol. 1 (2007), pp. I – 313–I – 316.

Frise, E.

J. Schindelin, I. Arganda-Carreras, E. Frise, V. Kaynig, M. Longair, T. Pietzsch, S. Preibisch, C. Rueden, S. Saalfeld, B. Schmid, J. Y. Tinevez, D. J. White, V. Hartenstein, K. Eliceiri, P. Tomancak, and A. Cardona,“Fiji: an open-source platform for biological-image analysis,” Nat. Methods 9, 676–682 (2012).
[Crossref] [PubMed]

Fujimoto, J. G.

Gatys, L. A.

L. A. Gatys, M. B. Alexander, and S. Ecker, “Image style transfer using convolutional neural networks,” The IEEE Conf. on Comput. Vis. Pattern Recognit. (CVPR) (2016).

Goodman, J.

J. Goodman, Speckle phenomena in optics: Theory and applications (W. H. Freeman, 2007).

Goodman, J. W.

Graetzel, C.

C. Graetzel, M. Suter, and M. Aschwanden, “Reducing laser speckle with electroactive polymer actuators,” Int. Soc. for Opt. Photonics p. 943004 (2015).

Harmany, Z.

J. Salmon, Z. Harmany, C. A. Deledalle, and R. Willett, “Poisson noise reduction with non-local pca,” J. Math. Imaging Vis. 48, 279–294 (2014).
[Crossref]

Hartenstein, V.

J. Schindelin, I. Arganda-Carreras, E. Frise, V. Kaynig, M. Longair, T. Pietzsch, S. Preibisch, C. Rueden, S. Saalfeld, B. Schmid, J. Y. Tinevez, D. J. White, V. Hartenstein, K. Eliceiri, P. Tomancak, and A. Cardona,“Fiji: an open-source platform for biological-image analysis,” Nat. Methods 9, 676–682 (2012).
[Crossref] [PubMed]

He, K.

C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE Transactions on Pattern Analysis Mach. Intell. 38, 295–307 (2016).
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Hojjatoleslami, A.

Huang, T.

T. Huang, G. Yang, and G. Tang, “A fast two-dimensional median filtering algorithm,” IEEE Transactions on Acoust. Speech, Signal Process. 27, 13–18 (1979).
[Crossref]

Jian, Z.

Katkovnik, V.

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-d transform-domain collaborative filtering,” IEEE Transactions on Image Process. 16, 2080–2095 (2007).
[Crossref]

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Color image denoising via sparse 3d collaborative filtering with grouping constraint in luminance-chrominance space,” in 2007 IEEE International Conference on Image Processing, vol. 1 (2007), pp. I – 313–I – 316.

Kaynig, V.

J. Schindelin, I. Arganda-Carreras, E. Frise, V. Kaynig, M. Longair, T. Pietzsch, S. Preibisch, C. Rueden, S. Saalfeld, B. Schmid, J. Y. Tinevez, D. J. White, V. Hartenstein, K. Eliceiri, P. Tomancak, and A. Cardona,“Fiji: an open-source platform for biological-image analysis,” Nat. Methods 9, 676–682 (2012).
[Crossref] [PubMed]

Ko, T. H.

Laissue, P. P.

Lee, H.

F. Agostinelli, M. R. Anderson, and H. Lee, “Adaptive multi-column deep neural networks with application to robust image denoising,” Proc. 26th Int. Conf. on Neural Inf. Process. Syst. - Vol.  1 pp. 1493–1501 (2013).

Lee, J.

Lew, M. D.

O. Liba, M. D. Lew, E. D. SoRelle, R. Dutta, D. Sen, D. M. Moshfeghi, S. Chu, and A. de la Zerda, “Speckle-modulating optical coherence tomography in living mice and humans,” Nat. Commun. 8, 15845 (2017).
[Crossref] [PubMed]

Li, S.

Li, Y.

Liba, O.

O. Liba, M. D. Lew, E. D. SoRelle, R. Dutta, D. Sen, D. M. Moshfeghi, S. Chu, and A. de la Zerda, “Speckle-modulating optical coherence tomography in living mice and humans,” Nat. Commun. 8, 15845 (2017).
[Crossref] [PubMed]

Longair, M.

J. Schindelin, I. Arganda-Carreras, E. Frise, V. Kaynig, M. Longair, T. Pietzsch, S. Preibisch, C. Rueden, S. Saalfeld, B. Schmid, J. Y. Tinevez, D. J. White, V. Hartenstein, K. Eliceiri, P. Tomancak, and A. Cardona,“Fiji: an open-source platform for biological-image analysis,” Nat. Methods 9, 676–682 (2012).
[Crossref] [PubMed]

Louchet, C.

C. Louchet and L. Moisan, “Total variation denoising using iterated conditional expectation,” in 2014 22nd European Signal Processing Conference (EUSIPCO), (2014), pp. 1592–1596.

R. Abergel, C. Louchet, L. Moisan, and T. Zeng, “Total variation restoration of images corrupted by poisson noise with iterated conditional expectations,” in Scale Space and Variational Methods in Computer Vision, J.-F. Aujol, M. Nikolova, and N. Papadakis, eds. (Springer International Publishing, Cham, 2015), pp. 178–190.

Loy, C. C.

C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE Transactions on Pattern Analysis Mach. Intell. 38, 295–307 (2016).
[Crossref]

Ma, Y.

Mahmood, F.

F. Mahmood, R. Chen, S. Sudarsky, D. Yu, and N. J. Durr, “Deep learning with cinematic rendering: Fine-tuning deep neural networks using photorealistic medical images,” Phys. Medicine Biol. 63, 18 (2018).
[Crossref]

F. Mahmood, R. Chen, and N. J. Durr, “Unsupervised reverse domain adaptation for synthetic medical images via adversarial training,” IEEE Transactions on Med. Imaging 37, 2572–2581 (2018).
[Crossref]

Mairal, J.

J. Mairal, M. Elad, and G. Sapiro, “Sparse representation for color image restoration,” IEEE Transactions on Image Process. 17, 53–69 (2008).
[Crossref]

Mascarenhas, N. D.

A. A. Bindilatti, M. A. Vieira, and N. D. Mascarenhas, “Poisson wiener filtering with non-local weighted parameter estimation using stochastic distances,” Signal Process. 144, 68–76 (2018).
[Crossref]

Meiniel, W.

W. Meiniel, J. Olivo-Marin, and E. D. Angelini, “Denoising of microscopy images: A review of the state-of-the-art, and a new sparsity-based method,” IEEE Transactions on Image Process. 27, 3842–3856 (2018).
[Crossref]

Mishra, A.

Moisan, L.

R. Abergel, C. Louchet, L. Moisan, and T. Zeng, “Total variation restoration of images corrupted by poisson noise with iterated conditional expectations,” in Scale Space and Variational Methods in Computer Vision, J.-F. Aujol, M. Nikolova, and N. Papadakis, eds. (Springer International Publishing, Cham, 2015), pp. 178–190.

C. Louchet and L. Moisan, “Total variation denoising using iterated conditional expectation,” in 2014 22nd European Signal Processing Conference (EUSIPCO), (2014), pp. 1592–1596.

Morel, J.-M.

A. Buades, B. Coll, and J.-M. Morel, “A Non-Local Algorithm for Image Denoising,” 2005 IEEE Comput. Soc. Conf. on Comput. Vis. Pattern Recognit. (CVPR’05)2, 60–65.

Moshfeghi, D. M.

O. Liba, M. D. Lew, E. D. SoRelle, R. Dutta, D. Sen, D. M. Moshfeghi, S. Chu, and A. de la Zerda, “Speckle-modulating optical coherence tomography in living mice and humans,” Nat. Commun. 8, 15845 (2017).
[Crossref] [PubMed]

Moskowitz, M. A.

A. K. Dunn, H. Bolay, M. A. Moskowitz, and D. A. Boas, “Dynamic Imaging of Cerebral Blood Flow Using Laser Speckle,” J. Cereb. Blood Flow & Metab. 21, 195–201 (2001).
[Crossref]

Olivo-Marin, J.

W. Meiniel, J. Olivo-Marin, and E. D. Angelini, “Denoising of microscopy images: A review of the state-of-the-art, and a new sparsity-based method,” IEEE Transactions on Image Process. 27, 3842–3856 (2018).
[Crossref]

Ono, S.

S. Ono and I. Yamada, “Color-line regularization for color artifact removal,” IEEE Transactions on Comput. Imaging 2, 204–217 (2016).
[Crossref]

Osher, S.

L. I. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Phys. D: Nonlinear Phenom. 60, 259–268 (1992).
[Crossref]

Peleg, T.

R. Rubinstein, T. Peleg, and M. Elad, “Analysis k-svd: A dictionary-learning algorithm for the analysis sparse model,” IEEE Transactions on Signal Process. 61, 661–677 (2013).
[Crossref]

Pietzsch, T.

J. Schindelin, I. Arganda-Carreras, E. Frise, V. Kaynig, M. Longair, T. Pietzsch, S. Preibisch, C. Rueden, S. Saalfeld, B. Schmid, J. Y. Tinevez, D. J. White, V. Hartenstein, K. Eliceiri, P. Tomancak, and A. Cardona,“Fiji: an open-source platform for biological-image analysis,” Nat. Methods 9, 676–682 (2012).
[Crossref] [PubMed]

Podoleanu, A. G.

Preibisch, S.

J. Schindelin, I. Arganda-Carreras, E. Frise, V. Kaynig, M. Longair, T. Pietzsch, S. Preibisch, C. Rueden, S. Saalfeld, B. Schmid, J. Y. Tinevez, D. J. White, V. Hartenstein, K. Eliceiri, P. Tomancak, and A. Cardona,“Fiji: an open-source platform for biological-image analysis,” Nat. Methods 9, 676–682 (2012).
[Crossref] [PubMed]

Rao, B.

Reintjes, J.

Rubinstein, R.

R. Rubinstein, T. Peleg, and M. Elad, “Analysis k-svd: A dictionary-learning algorithm for the analysis sparse model,” IEEE Transactions on Signal Process. 61, 661–677 (2013).
[Crossref]

Rudin, L. I.

L. I. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Phys. D: Nonlinear Phenom. 60, 259–268 (1992).
[Crossref]

Rueden, C.

J. Schindelin, I. Arganda-Carreras, E. Frise, V. Kaynig, M. Longair, T. Pietzsch, S. Preibisch, C. Rueden, S. Saalfeld, B. Schmid, J. Y. Tinevez, D. J. White, V. Hartenstein, K. Eliceiri, P. Tomancak, and A. Cardona,“Fiji: an open-source platform for biological-image analysis,” Nat. Methods 9, 676–682 (2012).
[Crossref] [PubMed]

Saalfeld, S.

J. Schindelin, I. Arganda-Carreras, E. Frise, V. Kaynig, M. Longair, T. Pietzsch, S. Preibisch, C. Rueden, S. Saalfeld, B. Schmid, J. Y. Tinevez, D. J. White, V. Hartenstein, K. Eliceiri, P. Tomancak, and A. Cardona,“Fiji: an open-source platform for biological-image analysis,” Nat. Methods 9, 676–682 (2012).
[Crossref] [PubMed]

Salinas, H. M.

H. M. Salinas and D. C. Fernandez, “Comparison of pde-based nonlinear diffusion approaches for image enhancement and denoising in optical coherence tomography,” IEEE Transactions on Med. Imaging 26, 761–771 (2007).
[Crossref]

Salmon, J.

J. Salmon, Z. Harmany, C. A. Deledalle, and R. Willett, “Poisson noise reduction with non-local pca,” J. Math. Imaging Vis. 48, 279–294 (2014).
[Crossref]

Sapiro, G.

J. Mairal, M. Elad, and G. Sapiro, “Sparse representation for color image restoration,” IEEE Transactions on Image Process. 17, 53–69 (2008).
[Crossref]

Schindelin, J.

J. Schindelin, I. Arganda-Carreras, E. Frise, V. Kaynig, M. Longair, T. Pietzsch, S. Preibisch, C. Rueden, S. Saalfeld, B. Schmid, J. Y. Tinevez, D. J. White, V. Hartenstein, K. Eliceiri, P. Tomancak, and A. Cardona,“Fiji: an open-source platform for biological-image analysis,” Nat. Methods 9, 676–682 (2012).
[Crossref] [PubMed]

Schmid, B.

J. Schindelin, I. Arganda-Carreras, E. Frise, V. Kaynig, M. Longair, T. Pietzsch, S. Preibisch, C. Rueden, S. Saalfeld, B. Schmid, J. Y. Tinevez, D. J. White, V. Hartenstein, K. Eliceiri, P. Tomancak, and A. Cardona,“Fiji: an open-source platform for biological-image analysis,” Nat. Methods 9, 676–682 (2012).
[Crossref] [PubMed]

Sen, D.

O. Liba, M. D. Lew, E. D. SoRelle, R. Dutta, D. Sen, D. M. Moshfeghi, S. Chu, and A. de la Zerda, “Speckle-modulating optical coherence tomography in living mice and humans,” Nat. Commun. 8, 15845 (2017).
[Crossref] [PubMed]

Sheikh, H.

Z. Wang, A. Bovik, H. Sheikh, and E. Simoncelli, “Image Quality Assessment: From Error Visibility to Structural Similarity,” IEEE Transactions on Image Process. 13, 600–612 (2004).
[Crossref]

Shi, F.

Simoncelli, E.

Z. Wang, A. Bovik, H. Sheikh, and E. Simoncelli, “Image Quality Assessment: From Error Visibility to Structural Similarity,” IEEE Transactions on Image Process. 13, 600–612 (2004).
[Crossref]

Sinha, A.

SoRelle, E. D.

O. Liba, M. D. Lew, E. D. SoRelle, R. Dutta, D. Sen, D. M. Moshfeghi, S. Chu, and A. de la Zerda, “Speckle-modulating optical coherence tomography in living mice and humans,” Nat. Commun. 8, 15845 (2017).
[Crossref] [PubMed]

Sudarsky, S.

F. Mahmood, R. Chen, S. Sudarsky, D. Yu, and N. J. Durr, “Deep learning with cinematic rendering: Fine-tuning deep neural networks using photorealistic medical images,” Phys. Medicine Biol. 63, 18 (2018).
[Crossref]

Suter, M.

C. Graetzel, M. Suter, and M. Aschwanden, “Reducing laser speckle with electroactive polymer actuators,” Int. Soc. for Opt. Photonics p. 943004 (2015).

Tang, G.

T. Huang, G. Yang, and G. Tang, “A fast two-dimensional median filtering algorithm,” IEEE Transactions on Acoust. Speech, Signal Process. 27, 13–18 (1979).
[Crossref]

Tang, X.

C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE Transactions on Pattern Analysis Mach. Intell. 38, 295–307 (2016).
[Crossref]

Thurstone, F.

J. G. Abbott and F. Thurstone, “Acoustic speckle: Theory and experimental analysis,” Ultrason. Imaging 1, 303–324 (1979).
[Crossref] [PubMed]

Tian, L.

Tinevez, J. Y.

J. Schindelin, I. Arganda-Carreras, E. Frise, V. Kaynig, M. Longair, T. Pietzsch, S. Preibisch, C. Rueden, S. Saalfeld, B. Schmid, J. Y. Tinevez, D. J. White, V. Hartenstein, K. Eliceiri, P. Tomancak, and A. Cardona,“Fiji: an open-source platform for biological-image analysis,” Nat. Methods 9, 676–682 (2012).
[Crossref] [PubMed]

Tomancak, P.

J. Schindelin, I. Arganda-Carreras, E. Frise, V. Kaynig, M. Longair, T. Pietzsch, S. Preibisch, C. Rueden, S. Saalfeld, B. Schmid, J. Y. Tinevez, D. J. White, V. Hartenstein, K. Eliceiri, P. Tomancak, and A. Cardona,“Fiji: an open-source platform for biological-image analysis,” Nat. Methods 9, 676–682 (2012).
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Tromberg, B. J.

Tupin, F.

C. Deledalle, F. Tupin, and L. Denis, “Poisson nl means: Unsupervised non local means for poisson noise,” in 2010 IEEE International Conference on Image Processing, (2010), pp. 801–804.
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Vieira, M. A.

A. A. Bindilatti, M. A. Vieira, and N. D. Mascarenhas, “Poisson wiener filtering with non-local weighted parameter estimation using stochastic distances,” Signal Process. 144, 68–76 (2018).
[Crossref]

Wang, Z.

Z. Wang, A. Bovik, H. Sheikh, and E. Simoncelli, “Image Quality Assessment: From Error Visibility to Structural Similarity,” IEEE Transactions on Image Process. 13, 600–612 (2004).
[Crossref]

White, D. J.

J. Schindelin, I. Arganda-Carreras, E. Frise, V. Kaynig, M. Longair, T. Pietzsch, S. Preibisch, C. Rueden, S. Saalfeld, B. Schmid, J. Y. Tinevez, D. J. White, V. Hartenstein, K. Eliceiri, P. Tomancak, and A. Cardona,“Fiji: an open-source platform for biological-image analysis,” Nat. Methods 9, 676–682 (2012).
[Crossref] [PubMed]

Willett, R.

J. Salmon, Z. Harmany, C. A. Deledalle, and R. Willett, “Poisson noise reduction with non-local pca,” J. Math. Imaging Vis. 48, 279–294 (2014).
[Crossref]

Wong, A.

Xiang, D.

Xue, Y.

Yamada, I.

S. Ono and I. Yamada, “Color-line regularization for color artifact removal,” IEEE Transactions on Comput. Imaging 2, 204–217 (2016).
[Crossref]

Yang, G.

T. Huang, G. Yang, and G. Tang, “A fast two-dimensional median filtering algorithm,” IEEE Transactions on Acoust. Speech, Signal Process. 27, 13–18 (1979).
[Crossref]

Yu, D.

F. Mahmood, R. Chen, S. Sudarsky, D. Yu, and N. J. Durr, “Deep learning with cinematic rendering: Fine-tuning deep neural networks using photorealistic medical images,” Phys. Medicine Biol. 63, 18 (2018).
[Crossref]

Yu, L.

Zeng, T.

R. Abergel, C. Louchet, L. Moisan, and T. Zeng, “Total variation restoration of images corrupted by poisson noise with iterated conditional expectations,” in Scale Space and Variational Methods in Computer Vision, J.-F. Aujol, M. Nikolova, and N. Papadakis, eds. (Springer International Publishing, Cham, 2015), pp. 178–190.

Zhu, W.

Appl. Opt. (1)

Biomed. Opt. Express (1)

IEEE Transactions on Acoust. Speech, Signal Process. (1)

T. Huang, G. Yang, and G. Tang, “A fast two-dimensional median filtering algorithm,” IEEE Transactions on Acoust. Speech, Signal Process. 27, 13–18 (1979).
[Crossref]

IEEE Transactions on Comput. Imaging (1)

S. Ono and I. Yamada, “Color-line regularization for color artifact removal,” IEEE Transactions on Comput. Imaging 2, 204–217 (2016).
[Crossref]

IEEE Transactions on Image Process. (5)

J. Mairal, M. Elad, and G. Sapiro, “Sparse representation for color image restoration,” IEEE Transactions on Image Process. 17, 53–69 (2008).
[Crossref]

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-d transform-domain collaborative filtering,” IEEE Transactions on Image Process. 16, 2080–2095 (2007).
[Crossref]

W. Meiniel, J. Olivo-Marin, and E. D. Angelini, “Denoising of microscopy images: A review of the state-of-the-art, and a new sparsity-based method,” IEEE Transactions on Image Process. 27, 3842–3856 (2018).
[Crossref]

J. M. Bioucas-Dias and M. A. T. Figueiredo, “Multiplicative noise removal using variable splitting and constrained optimization,” IEEE Transactions on Image Process. 19, 1720–1730 (2010).
[Crossref]

Z. Wang, A. Bovik, H. Sheikh, and E. Simoncelli, “Image Quality Assessment: From Error Visibility to Structural Similarity,” IEEE Transactions on Image Process. 13, 600–612 (2004).
[Crossref]

IEEE Transactions on Med. Imaging (2)

H. M. Salinas and D. C. Fernandez, “Comparison of pde-based nonlinear diffusion approaches for image enhancement and denoising in optical coherence tomography,” IEEE Transactions on Med. Imaging 26, 761–771 (2007).
[Crossref]

F. Mahmood, R. Chen, and N. J. Durr, “Unsupervised reverse domain adaptation for synthetic medical images via adversarial training,” IEEE Transactions on Med. Imaging 37, 2572–2581 (2018).
[Crossref]

IEEE Transactions on Pattern Analysis Mach. Intell. (1)

C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE Transactions on Pattern Analysis Mach. Intell. 38, 295–307 (2016).
[Crossref]

IEEE Transactions on Signal Process. (2)

M. Aharon, M. Elad, and A. Bruckstein, “K-svd: An algorithm for designing overcomplete dictionaries for sparse representation,” IEEE Transactions on Signal Process. 54, 4311–4322 (2006).
[Crossref]

R. Rubinstein, T. Peleg, and M. Elad, “Analysis k-svd: A dictionary-learning algorithm for the analysis sparse model,” IEEE Transactions on Signal Process. 61, 661–677 (2013).
[Crossref]

J. Biomed. Opt. (1)

M. R. Christopher Glazowski, “Optimal detection pinhole for lowering speckle noise while maintaining adequate optical sectioning in confocal reflectance microscopes,” J. Biomed. Opt. 17, 085001 (2012).
[Crossref] [PubMed]

J. Cereb. Blood Flow & Metab. (1)

A. K. Dunn, H. Bolay, M. A. Moskowitz, and D. A. Boas, “Dynamic Imaging of Cerebral Blood Flow Using Laser Speckle,” J. Cereb. Blood Flow & Metab. 21, 195–201 (2001).
[Crossref]

J. Math. Imaging Vis. (1)

J. Salmon, Z. Harmany, C. A. Deledalle, and R. Willett, “Poisson noise reduction with non-local pca,” J. Math. Imaging Vis. 48, 279–294 (2014).
[Crossref]

J. Opt. Soc. Am. (1)

Nat. Commun. (1)

O. Liba, M. D. Lew, E. D. SoRelle, R. Dutta, D. Sen, D. M. Moshfeghi, S. Chu, and A. de la Zerda, “Speckle-modulating optical coherence tomography in living mice and humans,” Nat. Commun. 8, 15845 (2017).
[Crossref] [PubMed]

Nat. Methods (1)

J. Schindelin, I. Arganda-Carreras, E. Frise, V. Kaynig, M. Longair, T. Pietzsch, S. Preibisch, C. Rueden, S. Saalfeld, B. Schmid, J. Y. Tinevez, D. J. White, V. Hartenstein, K. Eliceiri, P. Tomancak, and A. Cardona,“Fiji: an open-source platform for biological-image analysis,” Nat. Methods 9, 676–682 (2012).
[Crossref] [PubMed]

Opt. Express (2)

Opt. Lett. (2)

Optica (2)

Phys. D: Nonlinear Phenom. (1)

L. I. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Phys. D: Nonlinear Phenom. 60, 259–268 (1992).
[Crossref]

Phys. Medicine Biol. (1)

F. Mahmood, R. Chen, S. Sudarsky, D. Yu, and N. J. Durr, “Deep learning with cinematic rendering: Fine-tuning deep neural networks using photorealistic medical images,” Phys. Medicine Biol. 63, 18 (2018).
[Crossref]

Proc. 26th Int. Conf. on Neural Inf. Process. Syst. (1)

F. Agostinelli, M. R. Anderson, and H. Lee, “Adaptive multi-column deep neural networks with application to robust image denoising,” Proc. 26th Int. Conf. on Neural Inf. Process. Syst. - Vol.  1 pp. 1493–1501 (2013).

Signal Process. (1)

A. A. Bindilatti, M. A. Vieira, and N. D. Mascarenhas, “Poisson wiener filtering with non-local weighted parameter estimation using stochastic distances,” Signal Process. 144, 68–76 (2018).
[Crossref]

Ultrason. Imaging (1)

J. G. Abbott and F. Thurstone, “Acoustic speckle: Theory and experimental analysis,” Ultrason. Imaging 1, 303–324 (1979).
[Crossref] [PubMed]

Other (15)

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Figures (6)

Fig. 1
Fig. 1 DeepLSR Architecture. (a) Training architecture for image-to-image translation-based laser speckle reduction using a conditional Generative Adversarial Network. A generator learns to transform between pairs of images acquired with coherent and incoherent illumination while a discriminator learns to classify input images as real or fake. (b) Once training is complete, the discriminator is discarded and the trained generator (DeepLSR) reduces laser speckle noise in images not previously seen by the generator.
Fig. 2
Fig. 2 Imaging setup for acquiring images with: (1) laser illumination (oLSR turned off), (2) laser illumination with optical laser speckle reduction (oLSR turned on), and (3) LED illumination.
Fig. 3
Fig. 3 DeepLSR compared to conventional speckle reduction methods. DeepLSR was trained on an assortment of images that represent a variety of textures, shapes, and bidirectional reflectance distribution functions. (a) Images of two test objects illuminated with laser illumination, laser illumination with optical speckle reduction (oLSR), median filtering, non-local means, K-SVD, CBM3D, DeepLSR applied to the laser illuminated image, DeepLSR+oLSR applied to the optically speckle reduced image (DeepLSR+oLSR), the target speckle-free image illuminated with a light-emitting diode (LED), and the speckle artifacts removed from the laser illuminated image by DeepLSR. (b) Modulation transfer functions for LED illumination and laser illumination with DeepLSR found using a slanted edge. (c) Images of a 1951 United States Air Force Target with each illumination strategy and laser illumination with DeepLSR.
Fig. 4
Fig. 4 Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) results from reserved test images of assorted objects and porcine tissues.
Fig. 5
Fig. 5 (a) Images of a test object for each speckle reduction technique. (b) The red channels from the color images were studied to assess speckle reduction in the absence of information from other channels. Line profiles from a reserved test image patch are reported, comparing image processing methods (NLM, CBM3D, DeepLSR) and optical methods (oLSR, DeepLSR+oLSR) to the input (Laser) and ground truth (LED) images.
Fig. 6
Fig. 6 DeepLSR applied to images of laser-illuminated ex-vivo porcine gastrointestinal tissues not previously seen by the network.

Tables (1)

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Table 1 Parameters for noise reduction methods

Equations (2)

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PSNR = 10 log 10 ( R 2 MSE )
SSIM ( x , y ) = ( 2 μ x μ y + C 1 ) ( 2 σ x y + C 2 ) ( μ x 2 + μ y 2 + C 1 ) ( σ x 2 + σ y 2 + C 2 )

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