  Burst super-resolution (SR) technique provides a possibility of restoring
rich details from low-quality images. However, since real world low-resolution
(LR) images in practical applications have multiple complicated and unknown
degradations, existing non-blind (e.g., bicubic) designed networks usually
suffer severe performance drop in recovering high-resolution (HR) images. In
this paper, we address the problem of reconstructing HR images from raw burst
sequences acquired from a modern handheld device. The central idea is a
kernel-guided strategy which can solve the burst SR problem with two steps:
kernel estimation and HR image restoration. The former estimates burst kernels
from raw inputs, while the latter predicts the super-resolved image based on
the estimated kernels. Furthermore, we introduce a pyramid kernel-aware
deformable alignment module which can effectively align the raw images with
consideration of the blurry priors. Extensive experiments on synthetic and
real-world datasets demonstrate that the proposed method can perform favorable
state-of-the-art performance in the burst SR problem. Our codes are available
at \url{https://github.com/shermanlian/KBNet}.
