Unsupervised Degradation Representation Learning for Blind Super-Resolution (2104.00416v1)

Abstract: Most existing CNN-based super-resolution (SR) methods are developed based on an assumption that the degradation is fixed and known (e.g., bicubic downsampling). However, these methods suffer a severe performance drop when the real degradation is different from their assumption. To handle various unknown degradations in real-world applications, previous methods rely on degradation estimation to reconstruct the SR image. Nevertheless, degradation estimation methods are usually time-consuming and may lead to SR failure due to large estimation errors. In this paper, we propose an unsupervised degradation representation learning scheme for blind SR without explicit degradation estimation. Specifically, we learn abstract representations to distinguish various degradations in the representation space rather than explicit estimation in the pixel space. Moreover, we introduce a Degradation-Aware SR (DASR) network with flexible adaption to various degradations based on the learned representations. It is demonstrated that our degradation representation learning scheme can extract discriminative representations to obtain accurate degradation information. Experiments on both synthetic and real images show that our network achieves state-of-the-art performance for the blind SR task. Code is available at: https://github.com/LongguangWang/DASR.

• The paper introduces an unsupervised degradation representation learning paradigm that bypasses fixed degradation assumptions to enhance blind super-resolution.
• The paper leverages contrastive learning within a degradation-aware network to derive robust, content-invariant representations for adaptive kernel prediction.
• The method achieves state-of-the-art performance on synthetic and real datasets by delivering higher PSNR scores and flexible adaptation to unknown degradations.

Unsupervised Degradation Representation Learning for Blind Super-Resolution

The paper presents an innovative approach to tackling the challenge of blind super-resolution (SR) by introducing an unsupervised degradation representation learning paradigm. This method aims to address the limitations of existing CNN-based SR models that often assume a fixed degradation pattern, such as bicubic downsampling, which leads to performance degradation when encountering real-world variabilities.

Key Contributions

The authors propose the concept of learning abstract degradation representations instead of explicitly estimating the degradation in pixel space. These representations are utilized within a specially designed Degradation-Aware SR (DASR) network. The DASR network is capable of adapting flexibly to various degradation types by leveraging the learned representation, ultimately enhancing SR performance across unknown degradations.

Methodology

1. Degradation Representation Learning: The method uses contrastive learning to derive discriminative degradation representations. By contrasting positive pairs (image patches with the same degradation) against negative pairs (patches with different degradations), the network builds a robust representation space tailored to distinguishing diverse degradations.
2. Degradation-Aware SR Network: The DASR network integrates the degradation representations to customize its response to the observed input degradation dynamically. This integration is achieved by predicting convolutional kernels and modulation coefficients from the representation, enabling adaptive feature processing and enhanced restoration quality.

Experimental Evaluation

The proposed approach is validated through comprehensive experiments on both synthetic and real image datasets. Key findings include:

• The DASR network achieves state-of-the-art performance across blind SR tasks, surpassing previous methods that rely heavily on explicit degradation estimation.
• The method demonstrates superior efficiency and accuracy in handling various degradation scenarios, confirmed by higher PSNR scores compared to existing techniques.
• The learned degradation representations are shown to be content-invariant, providing consistent performance across images with different content but identical degradations.

Implications and Future Directions

This research contributes significantly to the field of image super-resolution by offering a more resilient and flexible solution to degradation variability. The use of contrastive learning for representation learning opens new avenues for further exploration in unsupervised learning environments within the domain.

Future research could explore integrating this method into other domains where degradation variability is a concern, or extend the framework to support more complex degradation models incorporating additional factors such as sensor noise or compression artifacts.

In conclusion, the paper presents a robust, efficient solution for blind super-resolution, advancing the capability of SR models to handle real-world variability without the reliance on predefined degradation assumptions.