Reliable detection of calcium waves in multiphoton imaging data is challenging because of the low signal-to-noise ratio and because of the unpredictability of the time and location of these spontaneous events. This paper describes our approach to calcium wave detection and reconstruction based on a modified multiscale vision model, an object detection framework based on the thresholding of wavelet coefficients and hierarchical trees of significant coefficients followed by nonlinear iterative partial object reconstruction, for the analysis of two-photon calcium imaging data. The framework is discussed in the context of detection and reconstruction of intercellular glial calcium waves. We extend the framework by a different decomposition algorithm and iterative reconstruction of the detected objects. Comparison with several popular state-of-the-art image denoising methods shows that performance of the multiscale vision model is similar in the denoising, but provides a better segmenation of the image into meaningful objects, whereas other methods need to be combined with dedicated thresholding and segmentation utilities.