  Modern handheld devices can acquire burst image sequence in a quick
succession. However, the individual acquired frames suffer from multiple
degradations and are misaligned due to camera shake and object motions. The
goal of Burst Image Restoration is to effectively combine complimentary cues
across multiple burst frames to generate high-quality outputs. Towards this
goal, we develop a novel approach by solely focusing on the effective
information exchange between burst frames, such that the degradations get
filtered out while the actual scene details are preserved and enhanced. Our
central idea is to create a set of pseudo-burst features that combine
complementary information from all the input burst frames to seamlessly
exchange information. However, the pseudo-burst cannot be successfully created
unless the individual burst frames are properly aligned to discount inter-frame
movements. Therefore, our approach initially extracts pre-processed features
from each burst frame and matches them using an edge-boosting burst alignment
module. The pseudo-burst features are then created and enriched using
multi-scale contextual information. Our final step is to adaptively aggregate
information from the pseudo-burst features to progressively increase resolution
in multiple stages while merging the pseudo-burst features. In comparison to
existing works that usually follow a late fusion scheme with single-stage
upsampling, our approach performs favorably, delivering state-of-the-art
performance on burst superresolution, burst low-light image enhancement, and
burst denoising tasks. The source code and pre-trained models are available at
\url{https://github.com/akshaydudhane16/BIPNet}.
