Inverse problems appear all across signal processing and involve the reconstruction of a degraded signal to match a clean ground truth signal. In imaging, this pertains to tasks like super-resolution (digital zoom), in-painting, deblurring, or denoising. For higher dimensional images, like light field images, this includes angular super-resolution, meaning the synthesis of new viewing angles from a set of reference views. Given that these inverse problems are often ill-posed, regularization is crucial. Deep learning excels for this and many neural network-based approaches are currently being investigated as regularizers in iterative procedures. This work leverages deep equilibrium models to train deep regularizers in iterative procedures via the corresponding fixed-point. The overall aim is to train neural networks that allow for high-quality image restoration and generation in the context of inverse problems.