Reproduce “Deep learning in motion deblurring: current status, benchmarks and future prospects” on OpenResearch

About this paper

Yawen Xiang, Heng Zhou, Chengyang Li, Fangwei Sun, Zhongbo Li, Yongqiang Xie

This review provides a comprehensive analysis of deep learning approaches for blind motion deblurring, classifying methods into CNN, RNN, GAN, and Transformer categories. It quantitatively benchmarks over 30 state-of-the-art algorithms on four datasets, finding Transformer-based methods like FFTformer achieve high performance, such as a 34.21% PSNR on GoPro, while also detailing efficiency trade-offs and future research avenues.

Key insights

• Deep learning methods have largely surpassed traditional techniques in blind motion deblurring by learning end-to-end mappings, eliminating the need for explicit blur kernel estimation.
• Different deep learning architectures offer distinct capabilities and trade-offs: Transformers excel at global context, CNNs at local features and efficiency, and RNNs at temporal dependencies, while GANs face challenges in objective metric performance due to training instabilities.
• A critical balance exists between deblurring model performance and efficiency, with top-performing Transformer-based models often incurring higher computational costs and parameter counts.

Summary by alphaXiv.