Nonlinear operations are becoming increasingly important in dataset processing applications. Since they are substantially more difficult to analyze, formulate and predict compared to linear operations, various innovative approaches have been proposed independently for low-level computer vision tasks. The integration of several approaches that rely on different mathematical tools (e.g., functional minimization, nonlinear PDEs, statistics and data analysis) is essential for obtaining high-quality results in real-life applications.