Objectives: Correction for lateral displacements of the imaged area is often a necessary first step of processing calcium imaging data, especially in awake animal studies. We address two problems: (1) image displacements (warps) can be poorly described by simple rigid-body translations or shifts and can be non-uniform across image; (2) due to fluorescence intensity changes single template image may not be optimal for a subset of the movie frames.

Methods: We address the first problem by using either a combined local/global algorithm of optical flow estimation or an original algorithm based on calculation of optical flow in image patches with global regularization. Both algorithms estimate smooth optical flow fields between a current image and a template image and allow for correction of large-scale displacements by employing a multiscale pyramidal approach. The second problem is solved by using a set of template images, obtained from clusters of image frames in low-dimensional PCA-based space. To allow for efficient storage of the estimated image warps, they can be represented as low-pass DCT coefficients or by other dictionary-based methods.

Conclusions: The proposed pipeline for motion correction of calcium timelapse imaging data is accurate, can represent non-rigid image distortions, robust to noisy data and allows for fast registration of large videos. The implementation is open-source and is programmed in Python, which provides for easy access and merging into downstream image processing workflows.