  Merging short-exposure frames can provide an image with reduced noise in low
light conditions. However, how best to align images before merging is an open
problem. To improve the performance of alignment, we propose an inertia-sensor
aided algorithm for smartphone burst photography, which takes rotation and
out-plane relative movement into account. To calculate homography between
frames, a three by three rotation matrix is calculated from gyro data recorded
by smartphone inertia sensor and three-dimensional translation vector are
estimated by matched feature points detected from two frames. The rotation
matrix and translations are combined to form the initial guess of homography.
An unscented Kalman filter is utilized to provide a more accurate homography
estimation. We have tested the algorithm on a variety of different scenes with
different camera relative motions. We compare the proposed method to benchmark
single-image and multi-image denoising methods with favorable results.
