  Benefited from the deep learning, image Super-Resolution has been one of the
most developing research fields in computer vision. Depending upon whether
using a discriminator or not, a deep convolutional neural network can provide
an image with high fidelity or better perceptual quality. Due to the lack of
ground truth images in real life, people prefer a photo-realistic image with
low fidelity to a blurry image with high fidelity. In this paper, we revisit
the classic example based image super-resolution approaches and come up with a
novel generative model for perceptual image super-resolution. Given that real
images contain various noise and artifacts, we propose a joint image denoising
and super-resolution model via Variational AutoEncoder. We come up with a
conditional variational autoencoder to encode the reference for dense feature
vector which can then be transferred to the decoder for target image denoising.
With the aid of the discriminator, an additional overhead of super-resolution
subnetwork is attached to super-resolve the denoised image with photo-realistic
visual quality. We participated the NTIRE2020 Real Image Super-Resolution
Challenge. Experimental results show that by using the proposed approach, we
can obtain enlarged images with clean and pleasant features compared to other
supervised methods. We also compared our approach with state-of-the-art methods
on various datasets to demonstrate the efficiency of our proposed unsupervised
super-resolution model.
