  Denoising and demosaicking are two essential steps to reconstruct a clean
full-color image from the raw data. Recently, joint denoising and demosaicking
(JDD) for burst images, namely JDD-B, has attracted much attention by using
multiple raw images captured in a short time to reconstruct a single
high-quality image. One key challenge of JDD-B lies in the robust alignment of
image frames. State-of-the-art alignment methods in feature domain cannot
effectively utilize the temporal information of burst images, where large
shifts commonly exist due to camera and object motion. In addition, the higher
resolution (e.g., 4K) of modern imaging devices results in larger displacement
between frames. To address these challenges, we design a differentiable
two-stage alignment scheme sequentially in patch and pixel level for effective
JDD-B. The input burst images are firstly aligned in the patch level by using a
differentiable progressive block matching method, which can estimate the offset
between distant frames with small computational cost. Then we perform implicit
pixel-wise alignment in full-resolution feature domain to refine the alignment
results. The two stages are jointly trained in an end-to-end manner. Extensive
experiments demonstrate the significant improvement of our method over existing
JDD-B methods. Codes are available at https://github.com/GuoShi28/2StageAlign.
