  It is a challenging task to recover sharp image from a single defocus blurry
image in real-world applications. On many modern cameras, dual-pixel (DP)
sensors create two-image views, based on which stereo information can be
exploited to benefit defocus deblurring. Despite the impressive results
achieved by existing DP defocus deblurring methods, the misalignment between DP
image views is still not studied, leaving room for improving DP defocus
deblurring. In this work, we propose a Dual-Pixel Alignment Network (DPANet)
for defocus deblurring. Generally, DPANet is an encoder-decoder with
skip-connections, where two branches with shared parameters in the encoder are
employed to extract and align deep features from left and right views, and one
decoder is adopted to fuse aligned features for predicting the sharp image. Due
to that DP views suffer from different blur amounts, it is not trivial to align
left and right views. To this end, we propose novel encoder alignment module
(EAM) and decoder alignment module (DAM). In particular, a correlation layer is
suggested in EAM to measure the disparity between DP views, whose deep features
can then be accordingly aligned using deformable convolutions. DAM can further
enhance the alignment of skip-connected features from encoder and deep features
in decoder. By introducing several EAMs and DAMs, DP views in DPANet can be
well aligned for better predicting latent sharp image. Experimental results on
real-world datasets show that our DPANet is notably superior to
state-of-the-art deblurring methods in reducing defocus blur while recovering
visually plausible sharp structures and textures.
