  This article presents a sliding window model for defocus deblurring, named
Swintormer, which achieves the best performance to date with remarkably low
memory usage. This method utilizes a diffusion model to generate latent prior
features, aiding in the restoration of more detailed images. Additionally, by
adapting the sliding window strategy, it incorporates specialized Transformer
blocks to enhance inference efficiency. The adoption of this new approach has
led to a substantial reduction in Multiply-Accumulate Operations (MACs) per
iteration, drastically cutting down memory requirements. In comparison to the
currently leading GRL method, our Swintormer model significantly reduces the
computational load that must depend on memory capacity, from 140.35 GMACs to
8.02 GMACs, while improving the Peak Signal-to-Noise Ratio (PSNR) for defocus
deblurring from 27.04 dB to 27.07 dB. This innovative technique enables the
processing of higher resolution images on memory-limited devices, vastly
broadening potential application scenarios. The article wraps up with an
ablation study, offering a comprehensive examination of how each network module
contributes to the final performance.The source code and model will be
available at the following website: https://github.com/bnm6900030/swintormer.
