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Speckle artifacts degrade image quality in virtually all modalities thatutilize coherent energy, including optical coherence tomography, reflectanceconfocal 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 asource domain of coherent illumination to a target domain of speckle-free,incoherent illumination. We apply this method to widefield images of objectsand tissues illuminated with a multi-wavelength laser, using light emittingdiode-illuminated images as ground truth. In images of gastrointestinaltissues, DeepLSR reduces laser speckle noise by 6.4 dB, compared to a 2.9 dBreduction from optimized non-local means processing, a 3.0 dB reduction fromBM3D, and a 3.7 dB reduction from an optical speckle reducer utilizing anoscillating diffuser. Further, DeepLSR can be combined with optical specklereduction to reduce speckle noise by 9.4 dB. This dramatic reduction in specklenoise may enable the use of coherent light sources in applications that requiresmall illumination sources and high-quality imaging, including medicalendoscopy.

DeepLSR: a deep learning approach for laser speckle reduction