Robust and Efficient Alignment of Calcium Imaging Data Through Simultaneous Low Rank and Sparse Decomposition

Junmo Cho, Seungjae Han, Eun-Seo Cho, Kijung Shin, Young-Gyu Yoon; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023, pp. 1939-1948

Accurate alignment of calcium imaging data, which is critical for the extraction of neuronal activity signals, is often hindered by the image noise and the neuronal activity itself. To address the problem, we propose an algorithm named REALS for robust and efficient batch image alignment through simultaneous geometric transformation and low rank and sparse decomposition. REALS is constructed upon our finding that the low rank subspace can be recovered via linear projection, which allows us to perform simultaneous image alignment and decomposition with gradient-based updates. REALS achieves orders-of magnitude improvement in terms of accuracy and speed compared to the state-of-the-art robust image alignment algorithms. In addition, we introduce two extended versions of REALS that achieve even higher accuracy than REALS under challenging conditions. First, multi-resolution REALS achieves up to 5 times higher alignment accuracy than REALS. Second, deformable REALS generalizes REALS for non-rigid registration. Furthermore, REALS can be combined with downstream tasks such as unsupervised image segmentation owing to its differentiability.