Abstract
The increasing popularity of computational light field
(LF) cameras has necessitated the need for tackling motion blur which is a ubiquitous phenomenon in hand-held
photography. The state-of-the-art method for blind deblurring of LFs of general 3D scenes is limited to handling
only downsampled LF, both in spatial and angular resolution. This is due to the computational overhead involved
in processing data-hungry full-resolution 4D LF altogether.
Moreover, the method warrants high-end GPUs for optimization and is ineffective for wide-angle settings and irregular camera motion. In this paper, we introduce a new blind
motion deblurring strategy for LFs which alleviates these
limitations significantly. Our model achieves this by isolating 4D LF motion blur across the 2D subaperture images,
thus paving the way for independent deblurring of these
subaperture images. Furthermore, our model accommodates common camera motion parameterization across the
subaperture images. Consequently, blind deblurring of any
single subaperture image elegantly paves the way for costeffective non-blind deblurring of the other subaperture images. Our approach is CPU-efficient computationally and
can effectively deblur full-resolution LFs