  We propose a regularization-based image restoration scheme for 2D images
recorded over time (2D+t). We design an infimal convolution-based
regularization function which we call spatio-temporal Adaptive Infimal
Convolution (STAIC) regularization. We formulate the infimal convolution in the
form of an additive decomposition of the 2D+t image such that the extent of
spatial and temporal smoothing is controlled in a spatially and temporally
varying manner. This makes the regularization adaptable to the local
characteristics of the motion leading to an improved ability to handle noise.
We also develop a minimization method for image reconstruction by using the
proposed form of regularization. We demonstrate the effectiveness of the
proposed regularization using TIRF images recorded over time and compare with
some selected existing regularizations.
