  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.
