We propose an efficient deep learning method for single image defocus deblurring (SIDD) by further exploring inverse kernel properties. Although the current inverse kernel method, i.e., kernel-sharing parallel atrous convolution (KPAC), can address spatially varying defocus blurs, it has difficulty in handling large blurs of this kind. To tackle this issue, we propose a Residual and Recursive Kernel-sharing Atrous Convolution (R$^2$KAC). R$^2$KAC builds on a significant observation of inverse kernels, that is, successive use of inverse-kernel-based deconvolutions with fixed size helps remove unexpected large blurs but produces ringing artifacts. Specifically, on top of kernel-sharing atrous convolutions used to simulate multi-scale inverse kernels, R$^2$KAC applies atrous convolutions recursively to simulate a large inverse kernel. Specifically, on top of kernel-sharing atrous convolutions, R$^2$KAC stacks atrous convolutions recursively to simulate a large inverse kernel. To further alleviate the contingent effect of recursive stacking, i.e., ringing artifacts, we add identity shortcuts between atrous convolutions to simulate residual deconvolutions. Lastly, a scale recurrent module is embedded in the R$^2$KAC network, leading to SR-R$^2$KAC, so that multi-scale information from coarse to fine is exploited to progressively remove the spatially varying defocus blurs. Extensive experimental results show that our method achieves the state-of-the-art performance.

我们提出了一种有效的深度学习方法，通过进一步探索反核函数的性质来进行单幅图像的缺陷消除（SIDD）。我们的方法建立在反核函数的重要观察基础上，即连续使用固定大小的反核函数解卷积有助于去除意外的大模糊，但会产生振铃伪影。通过在模拟多尺度反核函数的基础上，我们的方法通过递归堆叠空洞卷积来模拟大的反核函数，并添加了残差解卷积来减轻振铃伪影。我们还嵌入了一个尺度重复模块以逐步去除空间变化的模糊效果，实验结果表明我们的方法达到了最先进的性能。

SR-R$^2$KAC：提高单图像去模糊的方法