SR-R$^2$KAC: Improving Single Image Defocus Deblurring
Peng Tang Zhiqiang Xu Pengfei Wei Xiaobin Hu Peilin Zhao Xin Cao Chunlai Zhou Tobias Lasser
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.