Progressive Feature Fusion Attention Dense Network for Speckle Noise Removal in OCT Images

Lirong Zeng, Mengxing Huang, Yuchun Li, Qiong Chen*, Hong Ning Dai*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

6 Citations (Scopus)


Although deep learning for Big Data analytics has achieved promising results in the field of optical coherence tomography (OCT) image denoising, the low recognition rate caused by complex noise distribution and a large number of redundant features is still a challenge faced by deep learning-based denoising methods. Moreover, the network with large depth will bring high computational complexity. To this end, we propose a progressive feature fusion attention dense network (PFFADN) for speckle noise removal in OCT images. We arrange densely connected dense blocks in the deep convolution network, and sequentially connect the shallow convolution feature map with the deep one extracted from each dense block to form a residual block. We add attention mechanism to the network to extract the key features and suppress the irrelevant ones. We fuse the output feature maps from all dense blocks and input them to the reconstruction output layer. We compare PFFADN with the state-of-the-art denoising algorithms on retinal OCT images. Experiments show that our method has better improvement in denoising performance.

Original languageEnglish
Number of pages10
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Publication statusE-pub ahead of print - 8 Sept 2022

Scopus Subject Areas

  • Applied Mathematics
  • Genetics
  • Biotechnology

User-Defined Keywords

  • Attention mechanism
  • deep learning
  • image denoising
  • optical coherence tomography
  • progressive feature fusion
  • speckle noise


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