  Recent studies construct deblurred neural radiance fields (DeRF) using dozens
of blurry images, which are not practical scenarios if only a limited number of
blurry images are available. This paper focuses on constructing DeRF from
sparse-view for more pragmatic real-world scenarios. As observed in our
experiments, establishing DeRF from sparse views proves to be a more
challenging problem due to the inherent complexity arising from the
simultaneous optimization of blur kernels and NeRF from sparse view.
Sparse-DeRF successfully regularizes the complicated joint optimization,
presenting alleviated overfitting artifacts and enhanced quality on radiance
fields. The regularization consists of three key components: Surface
smoothness, helps the model accurately predict the scene structure utilizing
unseen and additional hidden rays derived from the blur kernel based on
statistical tendencies of real-world; Modulated gradient scaling, helps the
model adjust the amount of the backpropagated gradient according to the
arrangements of scene objects; Perceptual distillation improves the perceptual
quality by overcoming the ill-posed multi-view inconsistency of image
deblurring and distilling the pre-filtered information, compensating for the
lack of clean information in blurry images. We demonstrate the effectiveness of
the Sparse-DeRF with extensive quantitative and qualitative experimental
results by training DeRF from 2-view, 4-view, and 6-view blurry images.
