Abstract. We propose a neural approach for fusing an arbitrary-length
burst of photographs suffering from severe camera shake and noise into
a sharp and noise-free image. Our novel convolutional architecture has a
simultaneous view of all frames in the burst, and by construction treats
them in an order-independent manner. This enables it to effectively detect and leverage subtle cues scattered across different frames, while ensuring that each frame gets a full and equal consideration regardless
of its position in the sequence. We train the network with richly varied
synthetic data consisting of camera shake, realistic noise, and other common imaging defects. The method demonstrates consistent state of the
art burst image restoration performance for highly degraded sequences
of real-world images, and extracts accurate detail that is not discernible
from any of the individual frames in isolation