Efficient Image Deblurring Networks based on Diffusion Models
Kang Chen Yuanjie Liu
This article introduces a sliding window model for defocus deblurring that
achieves the best performance to date with extremely low memory usage. Named
Swintormer, the method utilizes a diffusion model to generate latent prior
features that assist in restoring more detailed images. It also extends the
sliding window strategy to specialized Transformer blocks for efficient
inference. Additionally, we have further optimized Multiply-Accumulate
operations (Macs). Compared to the currently top-performing GRL method, our
Swintormer model drastically reduces computational complexity from 140.35 GMACs
to 8.02 GMacs, while also improving the Signal-to-Noise Ratio (SNR) for defocus
deblurring from 27.04 dB to 27.07 dB. This new method allows for the processing
of higher resolution images on devices with limited memory, significantly
expanding potential application scenarios. The article concludes with an
ablation study that provides an in-depth analysis of the impact of each network
module on final performance. The source code and model will be available at the
following website: https://github.com/bnm6900030/swintormer.