DeepLSR: Deep learning approach for laser speckle reduction

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.

[1]  Alexei A. Efros,et al.  Image-to-Image Translation with Conditional Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Thomas S. Huang,et al.  A fast two-dimensional median filtering algorithm , 1979 .

[3]  Elsa D. Angelini,et al.  Denoising of Microscopy Images: A Review of the State-of-the-Art, and a New Sparsity-Based Method , 2018, IEEE Transactions on Image Processing.

[4]  Bahram Javidi,et al.  Three-dimensional speckle-noise reduction by using coherent integral imaging. , 2009, Optics letters.

[5]  H. M. Salinas,et al.  Comparison of PDE-Based Nonlinear Diffusion Approaches for Image Enhancement and Denoising in Optical Coherence Tomography , 2007, IEEE Transactions on Medical Imaging.

[6]  Florence Tupin,et al.  Poisson NL means: Unsupervised non local means for Poisson noise , 2010, 2010 IEEE International Conference on Image Processing.

[7]  Lionel Moisan,et al.  Total Variation denoising using iterated conditional expectation , 2014, 2014 22nd European Signal Processing Conference (EUSIPCO).

[8]  Leon A. Gatys,et al.  Image Style Transfer Using Convolutional Neural Networks , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  Honglak Lee,et al.  Adaptive Multi-Column Deep Neural Networks with Application to Robust Image Denoising , 2013, NIPS.

[10]  Alan L. Yuille,et al.  Rethinking Monocular Depth Estimation with Adversarial Training , 2018, ArXiv.

[11]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[12]  José M. Bioucas-Dias,et al.  Multiplicative Noise Removal Using Variable Splitting and Constrained Optimization , 2009, IEEE Transactions on Image Processing.

[13]  Rebecca Willett,et al.  Poisson Noise Reduction with Non-local PCA , 2012, Journal of Mathematical Imaging and Vision.

[14]  M. Bashkansky,et al.  Statistics and reduction of speckle in optical coherence tomography. , 2000, Optics letters.

[15]  Xinjian Chen,et al.  Speckle noise reduction in optical coherence tomography images based on edge-sensitive cGAN. , 2018, Biomedical optics express.

[16]  Xiaohua Zhai,et al.  The GAN Landscape: Losses, Architectures, Regularization, and Normalization , 2018, ArXiv.

[17]  J. Goodman.  Some fundamental properties of speckle , 1976 .

[18]  Bruce J. Tromberg,et al.  Three-dimensional speckle suppression in optical coherence tomography based on the curvelet transform , 2010, Optics express.

[19]  Xiaoou Tang,et al.  Image Super-Resolution Using Deep Convolutional Networks , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Lei Tian,et al.  Deep speckle correlation: a deep learning approach toward scalable imaging through scattering media , 2018, Optica.

[21]  Jean-Michel Morel,et al.  A non-local algorithm for image denoising , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[22]  Shunsuke Ono,et al.  Color-Line Regularization for Color Artifact Removal , 2016, IEEE Transactions on Computational Imaging.

[23]  Tae Joong Eom,et al.  Speckle reduction using an artificial neural network algorithm. , 2013, Applied optics.

[24]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[25]  Nam Ik Cho,et al.  Block-Matching Convolutional Neural Network for Image Denoising , 2017, ArXiv.

[26]  Karen O. Egiazarian,et al.  Color Image Denoising via Sparse 3D Collaborative Filtering with Grouping Constraint in Luminance-Chrominance Space , 2007, 2007 IEEE International Conference on Image Processing.

[27]  Tieyong Zeng,et al.  Total Variation Restoration of Images Corrupted by Poisson Noise with Iterated Conditional Expectations , 2015, SSVM.

[28]  Michael Elad,et al.  Analysis K-SVD: A Dictionary-Learning Algorithm for the Analysis Sparse Model , 2013, IEEE Transactions on Signal Processing.

[29]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[30]  L. Rudin,et al.  Nonlinear total variation based noise removal algorithms , 1992 .

[31]  F. L. Thurstone,et al.  Acoustic Speckle: Theory and Experimental Analysis , 1979 .

[32]  Jianlin Gao,et al.  Probability-based non-local means filter for speckle noise suppression in optical coherence tomography images. , 2016, Optics letters.

[33]  Faisal Mahmood,et al.  Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Training , 2017, IEEE Transactions on Medical Imaging.

[34]  Alessandro Foi,et al.  Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering , 2007, IEEE Transactions on Image Processing.

[35]  M. Moskowitz,et al.  Dynamic Imaging of Cerebral Blood Flow Using Laser Speckle , 2001, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[36]  Sandra Sudarsky,et al.  Deep learning with cinematic rendering: fine-tuning deep neural networks using photorealistic medical images , 2018, Physics in medicine and biology.

[37]  A. Bruckstein,et al.  K-SVD : An Algorithm for Designing of Overcomplete Dictionaries for Sparse Representation , 2005 .

[38]  Alexander Wong,et al.  General Bayesian estimation for speckle noise reduction in optical coherence tomography retinal imagery. , 2010, Optics express.

[39]  George Barbastathis,et al.  Imaging through glass diffusers using densely connected convolutional networks , 2017, Optica.

[40]  Milind Rajadhyaksha,et al.  Optimal detection pinhole for lowering speckle noise while maintaining adequate optical sectioning in confocal reflectance microscopes. , 2012, Journal of biomedical optics.

[41]  Yuichi Yoshida,et al.  Spectral Normalization for Generative Adversarial Networks , 2018, ICLR.

[42]  J. Goodman.  Speckle Phenomena in Optics: Theory and Applications , 2020 .

[43]  J. Fujimoto,et al.  Speckle reduction in optical coherence tomography images by use of a spatially adaptive wavelet filter. , 2004, Optics letters.

[44]  Joseph W. Goodman.  Optical Methods for Suppressing Speckle , 2020 .

[45]  Chauncey Graetzel,et al.  Reducing laser speckle with electroactive polymer actuators , 2015, Smart Structures.

[46]  Michael Elad,et al.  Sparse Representation for Color Image Restoration , 2008, IEEE Transactions on Image Processing.

[47]  Johannes E. Schindelin,et al.  Fiji: an open-source platform for biological-image analysis , 2012, Nature Methods.

[48]  M. Elad,et al.  $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation , 2006, IEEE Transactions on Signal Processing.

[49]  Bruce J. Tromberg,et al.  Three-dimensional speckle suppression in optical coherence tomography based on the curvelet transform , 2010, BiOS.

[50]  Soumith Chintala,et al.  Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.

[51]  Orly Liba,et al.  Speckle-modulating optical coherence tomography in living mice and humans , 2017, Nature Communications.

[52]  Nelson D. A. Mascarenhas,et al.  Poisson Wiener filtering with non-local weighted parameter estimation using stochastic distances , 2018, Signal Process..