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.

翻译：本文提出了一种用于散焦去模糊的滑动窗口模型，命名为Swintormer，该模型以极低的内存占用实现了迄今最佳性能。该方法利用扩散模型生成潜在先验特征，以协助恢复更精细的图像细节。同时，通过采用滑动窗口策略，模型引入了专用Transformer模块以提升推理效率。这一新方法的应用使得每次迭代的乘积累加运算量大幅降低，从而显著减少了内存需求。与当前领先的GRL方法相比，本研究的Swintormer模型将依赖内存容量的计算负载从140.35 GMACs降低至8.02 GMACs，同时将散焦去模糊的峰值信噪比从27.04 dB提升至27.07 dB。此项创新技术使得内存受限设备能够处理更高分辨率的图像，极大拓展了潜在应用场景。文章最后通过消融实验，系统分析了各网络模块对最终性能的贡献。源代码与模型将在以下网站发布：https://github.com/bnm6900030/swintormer。