R²MRF: Defocus Blur Detection via Recurrently Refining Multi-Scale Residual Features


  • Chang Tang China University of Geosciences
  • Xinwang Liu National University of Defense Technology
  • Xinzhong Zhu Zhejiang Normal University
  • En Zhu National University of Defense Technology
  • Kun Sun China University of Geosciences
  • Pichao Wang China University of Geosciences
  • Lizhe Wang China University of Geosciences
  • Albert Zomaya The University of Sydney




Defocus blur detection aims to separate the in-focus and out-of-focus regions in an image. Although attracting more and more attention due to its remarkable potential applications, there are still several challenges for accurate defocus blur detection, such as the interference of background clutter, sensitivity to scales and missing boundary details of defocus blur regions. In order to address these issues, we propose a deep neural network which Recurrently Refines Multi-scale Residual Features (R2MRF) for defocus blur detection. We firstly extract multi-scale deep features by utilizing a fully convolutional network. For each layer, we design a novel recurrent residual refinement branch embedded with multiple residual refinement modules (RRMs) to more accurately detect blur regions from the input image. Considering that the features from bottom layers are able to capture rich low-level features for details preservation while the features from top layers are capable of characterizing the semantic information for locating blur regions, we aggregate the deep features from different layers to learn the residual between the intermediate prediction and the ground truth for each recurrent step in each residual refinement branch. Since the defocus degree is sensitive to image scales, we finally fuse the side output of each branch to obtain the final blur detection map. We evaluate the proposed network on two commonly used defocus blur detection benchmark datasets by comparing it with other 11 state-of-the-art methods. Extensive experimental results with ablation studies demonstrate that R2MRF consistently and significantly outperforms the competitors in terms of both efficiency and accuracy.




How to Cite

Tang, C., Liu, X., Zhu, X., Zhu, E., Sun, K., Wang, P., Wang, L., & Zomaya, A. (2020). R²MRF: Defocus Blur Detection via Recurrently Refining Multi-Scale Residual Features. Proceedings of the AAAI Conference on Artificial Intelligence, 34(07), 12063-12070. https://doi.org/10.1609/aaai.v34i07.6884



AAAI Technical Track: Vision