Honeycomb Artifact Removal Using Convolutional Neural Network for Fiber Bundle Imaging

Honeycomb Artifact Removal Using Convolutional Neural Network for Fiber Bundle Imaging

Blog Article

We present a new deep Vitamins learning framework for removing honeycomb artifacts yielded by optical path blocking of cladding layers in fiber bundle imaging.The proposed framework, HAR-CNN, provides an end-to-end mapping from a raw fiber bundle image to an artifact-free image via a convolution neural network (CNN).The synthesis of honeycomb patterns on ordinary images allows conveniently learning and validating the network without the enormous ground truth collection Canvas Prints by extra hardware setups.As a result, HAR-CNN shows significant performance improvement in honeycomb pattern removal and also detailed preservation for the 1961 USAF chart sample, compared with other conventional methods.Finally, HAR-CNN is GPU-accelerated for real-time processing and enhanced image mosaicking performance.