In this story, Defocus and Motion Blur Detection with Deep Contextual Features, Kim JCGF’18, by POSTECH, and DGIST, is reviewed. In this paper:
- A deep encoder-decoder U-Net-like network with long residual skip-connections and multi-scale reconstruction loss to exploit high-level contextual features as well as low-level structural features.
- A synthetic dataset is constructed that consists of complex scenes with both motion and defocus blur.
This is a paper in 2018 JCGF, Computer Graphics Forum. (Sik-Ho Tsang @ Medium)