Functional Arterial Spin Labeling (fASL) provides important information of perfusion changes over time and is therefore suitable for detecting neuronal activation due to cognitive functions or motor tasks. However, the low signal to noise ratio of ASL images restrains its application in clinical and research areas. In this study we propose a method for denoising fASL data using infimal convolution of total generalized variations (ICTGV). Compared to standard Gaussian denoising ICTGV denoising incorporates spatial and temporal information of the perfusion weighted time series. This leads to a substantial improvement in noise-suppression for fASL data.