Fusing Blur Detection Results From Multiscale AlexNet-Like Networks, Obtain Better Results
In this story, Multiscale blur detection by learning discriminative deep features, BDNet, by Tianjin University, and Civil Aviation University of China, is reviewed. In this paper:
- A simple yet effective 6-layer CNN model, with 5 layers for feature extraction and 1 for binary classification is proposed, which can faithfully produce patch-level blur likelihood.
- The network is applied at three coarse-to-fine scales. The multiscale blur likelihood maps optimally fused to generate better blur detection.
This is a paper in 2018 JNEUCOM with over 20 citations where JNEUCOM is a journal in Elsevier with a high impact factor of 4.438. (Sik-Ho Tsang @ Medium)