mlsanityData to 7 Oct 2026

Attention! Stay Focus!

arXiv 2104.07925 ↗First posted 16 Apr 2021eess.IV cs.CVcited by 3

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Abstract (arXiv)

We develop a deep convolutional neural networks(CNNs) to deal with the blurry artifacts caused by the defocus of the camera using dual-pixel images. Specifically, we develop a double attention network which consists of attentional encoders, triple locals and global local modules to effectively extract useful information from each image in the dual-pixels and select the useful information from each image and synthesize the final output image. We demonstrate the effectiveness of the proposed deblurring algorithm in terms of both qualitative and quantitative aspects by evaluating on the test set in the NTIRE 2021 Defocus Deblurring using Dual-pixel Images Challenge. The code, and trained models are available at https://github.com/tuvovan/ATTSF.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 5 of its 17 references that are in the map. First posted on arXiv 16 Apr 2021.

Builds on

5 of 17 references.

References · 5

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,590
  3. 2017Non-local Neural Networkscited by 2,290
  4. 2020Defocus Deblurring Using Dual-Pixel Datacited by 138
  5. 2017A Unified Approach of Multi-scale Deep and Hand-crafted Features for Defocus Estimationcited by 25

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Cited by year

Citing papers per year: 2021: 1; 2022: 2.

3 papers.