Structure-Preserving Image Super-Resolution (2109.12530v1)

Abstract: Structures matter in single image super-resolution (SISR). Benefiting from generative adversarial networks (GANs), recent studies have promoted the development of SISR by recovering photo-realistic images. However, there are still undesired structural distortions in the recovered images. In this paper, we propose a structure-preserving super-resolution (SPSR) method to alleviate the above issue while maintaining the merits of GAN-based methods to generate perceptual-pleasant details. Firstly, we propose SPSR with gradient guidance (SPSR-G) by exploiting gradient maps of images to guide the recovery in two aspects. On the one hand, we restore high-resolution gradient maps by a gradient branch to provide additional structure priors for the SR process. On the other hand, we propose a gradient loss to impose a second-order restriction on the super-resolved images, which helps generative networks concentrate more on geometric structures. Secondly, since the gradient maps are handcrafted and may only be able to capture limited aspects of structural information, we further extend SPSR-G by introducing a learnable neural structure extractor (NSE) to unearth richer local structures and provide stronger supervision for SR. We propose two self-supervised structure learning methods, contrastive prediction and solving jigsaw puzzles, to train the NSEs. Our methods are model-agnostic, which can be potentially used for off-the-shelf SR networks. Experimental results on five benchmark datasets show that the proposed methods outperform state-of-the-art perceptual-driven SR methods under LPIPS, PSNR, and SSIM metrics. Visual results demonstrate the superiority of our methods in restoring structures while generating natural SR images. Code is available at https://github.com/Maclory/SPSR.

• The paper introduces a novel SPSR-G approach that uses gradient guidance to convert low-resolution gradient maps into high-resolution outputs while preserving geometric structures.
• It extends the methodology with a Neural Structure Extractor trained via self-supervised techniques like contrastive prediction and jigsaw puzzles to capture richer local features.
• Experimental validation on five benchmark datasets shows superior performance in LPIPS, PSNR, and SSIM metrics, confirming enhanced structural fidelity in image restoration.

Analysis of "Structure-Preserving Image Super-Resolution"

The paper "Structure-Preserving Image Super-Resolution" introduces a novel approach to single image super-resolution (SISR) that addresses the issue of structural distortion in super-resolved images. Utilizing the power of generative adversarial networks (GANs), this research aims to enhance image restoration while preserving geometric structures.

Key Contributions

1. Gradient Guidance:
   • The paper proposes a structure-preserving super-resolution method (SPSR-G) that incorporates gradient guidance. This approach uses gradient maps of images to guide the super-resolution process.
   • The method consists of a gradient branch that translates low-resolution (LR) gradient maps into high-resolution (HR) counterparts, providing additional structure priors for the SR process.
   • A gradient loss is introduced to impose second-order constraints, helping generative networks focus on geometric structures.
2. Neural Structure Extractor (NSE):
   • Recognizing the limitations of handcrafted gradient maps, the authors extend the method by introducing a learnable neural structure extractor (NSE).
   • Two self-supervised learning techniques, contrastive prediction and solving jigsaw puzzles, are used to train the NSEs, capturing richer local structures.
   • The proposed methods are model-agnostic and can be applied to off-the-shelf SR networks.
3. Experimental Validation:
   • The effectiveness of the proposed methods is validated on five benchmark datasets, showing superior performance under LPIPS, PSNR, and SSIM metrics.
   • Visual results demonstrate improved structure restoration and perceptual quality, supporting the quantitative findings.

Theoretical and Practical Implications

The introduction of gradient guidance and structure extraction presents a theoretical advancement in handling the ill-posed nature of SISR. By incorporating structural constraints, the model advances beyond mere pixel fidelity, achieving enhanced geometric consistency in reconstructed images. This approach bridges the gap between traditional PSNR-oriented methods and perceptual-driven techniques like GANs, which often sacrifice structural integrity for visual realism.

Practically, this research enhances the applicability of SISR in fields requiring detailed structural preservation, such as medical imaging and forensic analysis. The methods proposed are adaptable and can be integrated into existing SISR frameworks, promoting widespread adoption.

Speculations on Future Developments

The development of structure-preserving techniques opens avenues for further exploration in multi-scale and multi-frame super-resolution tasks, where structural consistency across various contexts is critical. Additionally, combining this approach with other advancements in self-supervised learning might yield even more robust SR methods with minimal reliance on large annotated datasets.

Future research could explore the integration of these methods with real-time processing capabilities, potentially impacting applications in video enhancement and live broadcasting. As computational resources become more accessible, the deployment of such advanced SISR algorithms in consumer devices appears promising.

In conclusion, the paper offers significant contributions to the field of super-resolution, demonstrating how structural preservation can be achieved through intelligent design of gradient and feature extraction mechanisms, backed by solid theoretical foundations and extensive experimental validation.