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.
Given a low-resolution (LR) image , and a scaling factor t, super-resolution aims to enhance image quality and generate a high-resolution (HR) image. The Tensorboard files for the results will be provided soon.
Given a noisy image, denoising aims to reduce the noise and recover the clean image. The Tensorboard files for the results will be provided soon.
Inpainting involves computing missing pixel values in the corrupted image in using the correspondence of a binary mask. The Tensorboard files for the results will be provided soon.
Given flash/no-flash image pair, the objective is to obtain a high-quality image which incorporates details of the scene from the flash image and ambient illumination from the no-flash image. The Tensorboard files for the results will be provided soon.