Attention! Stay Focus!

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.