STAIC regularization for 2D spatio-temporal image reconstruction

We propose a regularization-based image restoration method for 2D images recorded over time (2D+t). We design an infimal convolution-based regularization function, which we call the spatio-temporal adaptive infimal convolution (STAIC) regularization. We formulate our regularization 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 it with some selected existing regularization methods.