Closed-loop Matters: Dual Regression Networks for Single Image Super-Resolution (2003.07018v4)

Abstract: Deep neural networks have exhibited promising performance in image super-resolution (SR) by learning a nonlinear mapping function from low-resolution (LR) images to high-resolution (HR) images. However, there are two underlying limitations to existing SR methods. First, learning the mapping function from LR to HR images is typically an ill-posed problem, because there exist infinite HR images that can be downsampled to the same LR image. As a result, the space of the possible functions can be extremely large, which makes it hard to find a good solution. Second, the paired LR-HR data may be unavailable in real-world applications and the underlying degradation method is often unknown. For such a more general case, existing SR models often incur the adaptation problem and yield poor performance. To address the above issues, we propose a dual regression scheme by introducing an additional constraint on LR data to reduce the space of the possible functions. Specifically, besides the mapping from LR to HR images, we learn an additional dual regression mapping estimates the down-sampling kernel and reconstruct LR images, which forms a closed-loop to provide additional supervision. More critically, since the dual regression process does not depend on HR images, we can directly learn from LR images. In this sense, we can easily adapt SR models to real-world data, e.g., raw video frames from YouTube. Extensive experiments with paired training data and unpaired real-world data demonstrate our superiority over existing methods.

• The paper introduces a dual regression mechanism that learns both LR-to-HR and HR-to-LR mappings, reducing ambiguity in the super-resolution process.
• The paper demonstrates the method's adaptability to real-world scenarios by effectively training on unpaired low-resolution data and preserving texture details.
• The paper substantiates its claims with theoretical analysis and comprehensive experiments, consistently outperforming state-of-the-art methods on standard benchmarks.

Overview of "Closed-loop Matters: Dual Regression Networks for Single Image Super-Resolution"

Image Super-Resolution (SR) is an integral task in computer vision with applications spanning from video streaming to medical imaging. The paper "Closed-loop Matters: Dual Regression Networks for Single Image Super-Resolution" tackles the longstanding issue of ill-posedness in SR tasks, where infinitely many High-Resolution (HR) images can correspond to a single Low-Resolution (LR) input. The authors propose a Dual Regression Network (DRN) framework to overcome existing limitations and enhance the robustness of SR models particularly in real-world scenarios where paired LR-HR data may not always be available.

Key Contributions

1. Dual Regression Scheme: The paper introduces a dual regression framework where a reverse mapping from HR to LR images is learned alongside the primary LR to HR mapping. This closed-loop mechanism constrains the possible solutions to the SR problem, thereby reducing the function space and improving the generalization capabilities of SR networks.
2. Adaptation to Real-world Data Without HR Access: The paper addresses SR tasks where HR counterparts are unavailable. By leveraging the dual regression mapping which independently adapts to LR data, the proposed method can learn from real-world video frames directly. This has practical significance for applications involving streaming and content enhancement where original HR data is absent.
3. Superiority Over State-of-the-art: The authors substantiate their claims through comprehensive experiments on both paired and unpaired datasets. The proposed DRN consistently outperforms state-of-the-art methods on standard benchmarks and demonstrates compelling visual improvements, especially in maintaining texture sharpness.
4. Theoretical Analysis: The paper provides a theoretical underpinning for the dual regression framework, showcasing that the generalization error bound for the dual regression approach can be significantly superior compared to standalone models. This analysis further reinforces the importance of the dual mapping in constraining the solution space.

Implications and Future Directions

The theoretical and experimental insights presented in this paper have several implications. Firstly, the closed-loop method forms a foundational basis for further research in ill-posed, inverse problems beyond SR, such as image denoising or medical image reconstruction. Secondly, the practical application in handling unpaired data opens avenues for the inclusion of diverse real-world datasets in training SR models, thereby increasing their applicability and robustness.

Looking forward, future developments could explore the extension of dual regression models to other domains or consider hybrid approaches integrating additional priors or contextual learning. Moreover, optimizing model efficiency to balance complexity and performance will remain a critical area, especially for deploying SR models on resource-constrained devices.

In conclusion, this paper provides a significant advancement in addressing fundamental challenges in single-image SR through dual regression. It proposes practical solutions for deploying SR in real-world scenarios where paired data is an unmet luxury, thus broadening the horizon of super-resolution applications.