Spatially-Variant Kernel for Optical Flow Under Low Signal-to-Noise Ratios Application to Microscopy

Local and global approaches can be identified as the two main classes of optical flow estimation methods. In this paper, we propose a framework to combine the advantages of these two principles, namely robustness to noise of the local approach and discontinuity preservation of the global approach. This is particularly crucial in biological imaging, where the noise produced by microscopes is one of the main issues for optical flow estimation. The idea is to adapt spatially the local support of the local parametric constraint in the combined local-global model [6]. To this end, we jointly estimate the motion field and the parameters of the spatial support. We apply our approach to the case of Gaussian filtering, and we derive efficient minimization schemes for usual data terms. The estimation of a spatially varying standard deviation map prevents from the smoothing of motion discontinuities, while ensuring robustness to noise. We validate our method for a standard model and demonstrate how a baseline approach with pixel-wise data term can be improved when integrated in our framework. The method is evaluated on the Middlebury benchmark with ground truth and on real fluorescence microscopy data.