A boosted degradation representation learning for blind image super-resolution

The significant gap between the assumed and actual degradation model will undoubtedly lead to a serious performance decrease for deep learning based image super -resolution (SR) methods. To solve this shortcoming, this paper presents an enhanced blind image super -resolution (EBSR) network based on unsupervised degradation representation learning. The proposed EBSR mainly is made up of two branching subnetworks. The first subnetwork is a residual -based degradation encoder, which is responsible to learn a high -dimensional abstract degradation representation vector from the input low -resolution (LR) image patches using contrast learning. Another branch is the degradation -aware fusion SR (DAFSR) network that completes the image SR task with the aid of the degradation representation vector learned by the encoder. To obtain an improved SR performance under various degradation settings, the degradation -aware fusion block (DAFB), the core block of DAFSR, has been elaborated, which embeds the degradation model information learned by the encoder into every layer of the network. Additionally, a shallow feature extraction block (SFEB) is constructed for DAFSR to extract global information from the LR image. The proposed EBSR can flexibly adapt to various degradation models. Extensive experimental results on synthetic datasets and real images show that the proposed EBSR is able to achieve a leading reconstruction performance over other blind SR algorithms.