Regularization for Inverse Problems and Machine Learning: A Tutorial - Beraldo, Roberto Gutierrez; Ferreira, Leonardo Alves; Suyama, Ricardo (Anais da Conference on Graphics, Patterns and Images (SIBGRAPI); 2025: Anais da XXXVIII Conference on Graphics, Patterns and Images, 2025)
In this tutorial, we explore different paradigms for solving image processing tasks such as denoising and deblurring. These tasks can be interpreted as ill-posed inverse problems, grounded in physical-mathematical models that require regularization to produce meaningful solutions. Alternatively, they can be framed as supervised regression tasks within a machine learning context, where regularization techniques are used to improve generalization to unseen data. We compare these two perspectives, highlighting their similarities and dif-ferences-particularly in how regularization is understood and applied in each framework.