TL;DR
This paper introduces a novel deep CNN with a double attention mechanism for defocus deblurring using dual-pixel images, demonstrating superior results on the NTIRE 2021 challenge.
Contribution
It proposes a new double attention network architecture specifically designed for dual-pixel image deblurring, improving information extraction and synthesis.
Findings
Effective in reducing defocus artifacts
Outperforms existing methods on NTIRE 2021 dataset
Available code and models facilitate reproducibility
Abstract
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.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
