  The Light Field (LF) deblurring task is a challenging problem as the blur
images are caused by different reasons like the camera shake and the object
motion. The single image deblurring method is a possible way to solve this
problem. However, since it deals with each view independently and cannot
effectively utilize and maintain the LF structure, the restoration effect is
usually not ideal. Besides, the LF blur is more complex because the degree is
affected by the views and depth. Therefore, we carefully designed a novel LF
deblurring network based on the LF blur characteristics. On one hand, since the
blur degree varies a lot in different views, we design a novel view adaptive
spatial convolution to deblur blurred LFs, which calculates the exclusive
convolution kernel for each view. On the other hand, because the blur degree
also varies with the depth of the object, a depth perception view attention is
designed to deblur different depth areas by selectively integrating information
from different views. Besides, we introduce an angular position embedding to
maintain the LF structure better, which ensures the model correctly restores
the view information. Quantitative and qualitative experimental results on
synthetic and real images show that the deblurring effect of our method is
better than other state-of-the-art methods.
