  Many real-world solutions for image restoration are learning-free and based
on handcrafted image priors such as self-similarity. Recently, deep-learning
methods that use training data have achieved state-of-the-art results in
various image restoration tasks (e.g., super-resolution and inpainting).
Ulyanov et al. bridge the gap between these two families of methods (CVPR 18).
They have shown that learning-free methods perform close to the
state-of-the-art learning-based methods (approximately 1 PSNR). Their approach
benefits from the encoder-decoder network. In this paper, we propose a
framework based on the multi-level extensions of the encoder-decoder network,
to investigate interesting aspects of the relationship between image
restoration and network construction independent of learning. Our framework
allows various network structures by modifying the following network
components: skip links, cascading of the network input into intermediate
layers, a composition of the encoder-decoder subnetworks, and network depth.
These handcrafted network structures illustrate how the construction of
untrained networks influence the following image restoration tasks: denoising,
super-resolution, and inpainting. We also demonstrate image reconstruction
using flash and no-flash image pairs. We provide performance comparisons with
the state-of-the-art methods for all the restoration tasks above.
