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.
Highlights
Laser illumination offers many advantages over incoherent light for imaging, including high power densities, efficient light generation, narrow spectral bandwidths, robust stability, long lifetimes, and fast triggering capabilities
These samples were illuminated using a red-green-blue laser for coherent illumination, the same laser with a commercial optical laser speckle reducer utilizing an oscillating diffuser, and an light-emitting diodes (LEDs) for incoherent illumination
Validation tests on assorted objects imaged with laser illumination (Fig. 3a) show that DeepLSR reduces speckle noise by 5.3 dB, compared to a 2.7 dB reduction by non-local means filtering, a 3.6 dB reduction from CBM3D, and a 4.4 dB reduction by optical laser speckle reducer (oLSR) (Fig. 4)
Summary
Laser illumination offers many advantages over incoherent light for imaging, including high power densities, efficient light generation, narrow spectral bandwidths, robust stability, long lifetimes, and fast triggering capabilities. We describe a method for effectively learning the distribution of speckle artifacts to target and reduce noise in images not previously seen by the network This technique relies on a training set of coherent- and incoherent-illuminated image pairs of a variety of objects to learn a transformation from speckled to speckle-free images. DeepLSR is novel in its use of a true incoherent source as a target ground truth, the use of a diverse set of objects for training a generalizable model, and in its application of deep learning to widefield laser-illumination images. We benchmark this approach against conventional speckle reduction methods on images of laser-illuminated objects previously unseen by the network.
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