CT-Bound: Fast Boundary Estimation From Noisy Images Via Hybrid
  Convolution and Transformer Neural Networks
Wei Xu Junjie Luo Qi Guo
We present CT-Bound, a fast boundary estimation method for noisy images using
a hybrid Convolution and Transformer neural network. The proposed architecture
decomposes boundary estimation into two tasks: local detection and global
regularization of image boundaries. It first estimates a parametric
representation of boundary structures only using the input image within a small
receptive field and then refines the boundary structure in the parameter domain
without accessing the input image. Because of this, a part of the network can
be easily trained using naive, synthetic images and still generalized to real
images, and the entire architecture is computationally efficient as the
boundary refinement is non-iterative and not in the image domain. Compared with
the previous highest accuracy methods, our experiment shows that CT-Bound is
100 times faster, producing comparably accurate, high-quality boundary and
color maps. We also demonstrate that CT-Bound can produce boundary and color
maps on real captured images without extra fine-tuning and real-time boundary
map and color map videos at ten frames per second.