Bilevel Learning Approaches in Variational Image
Explore bilevel optimization approaches for variational image denoising in this 40-minute lecture by Juan Carlos De los Reyes. Delve into the mathematical foundations of determining noise models in corrupted images using supervised machine learning techniques. Examine differentiability properties of solution operators, derive Karush-Kuhn-Tucker systems for optimality conditions, and learn about fast Newton-type algorithms for numerical problem-solving. Compare the performance of different cost functionals and local regularizers, and investigate the extension to non-local variational models. Gain insights into the challenges of implementing these approaches and evaluate the effectiveness of local and non-local denoising models through test image comparisons.