Attention! Stay Focus!
This work addresses defocus deblurring for photography and imaging applications, presenting an incremental improvement with a novel network architecture.
The paper tackles the problem of blurry artifacts from camera defocus by developing a deep convolutional neural network using dual-pixel images, achieving effectiveness demonstrated through qualitative and quantitative evaluation on the NTIRE 2021 challenge test set.
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.