Regularization methods are a key tool in the solution of inverse problems. They are used to
introduce prior knowledge and allow a robust approximation of ill-posed (pseudo-) inverses …

The recent advances in hardware and imaging systems made the digital cameras
ubiquitous. Although the development of hardware has steadily improved the quality of …

Image restoration is a long-standing problem in low-level computer vision with many
interesting applications. We describe a flexible learning framework based on the concept of …

A large number of imaging problems reduce to the optimization of a cost function, with
typical structural properties. The aim of this paper is to describe the state of the art in …

Bilevel optimization has been recently revisited for designing and analyzing algorithms in
hyperparameter tuning and meta learning tasks. However, due to its nested structure …

We design a novel network architecture for learning discriminative image models that are
employed to efficiently tackle the problem of grayscale and color image denoising. Based on …

Abstract Analysis sparse representation (ASR) and synthesis sparse representation (SSR)
are two representative approaches for sparsity-based image modeling. An image is …

For several decades, image restoration remains an active research topic in low-level
computer vision and hence new approaches are constantly emerging. However, many …

Bilevel optimization problems are hierarchical optimization problems where the feasible
region of the so-called upper level problem is restricted by the graph of the solution set …

The emergence of deep-learning-based methods to solve image-reconstruction problems
has enabled a significant increase in quality. Unfortunately, these new methods often lack …