🤖 AI Summary
Real-world super-resolution (SR) faces two key challenges: (i) modeling complex, unknown degradations—such as spatially varying blur, heterogeneous noise, and color gamut shifts—using only unpaired low-resolution (LR) images; and (ii) the domain gap between synthetically generated and real-world degradations. Method: We propose TripleGAN, a fully unsupervised framework comprising three cascaded GANs: FirstGAN calibrates blur priors; SecondGAN performs domain-specific degradation transfer; and ThirdGAN reconstructs high-resolution (HR) images using pseudo-real, degradation-aligned training data. Contribution/Results: To our knowledge, this is the first method to jointly model implicit degradations and synthesize high-fidelity, degradation-matched training data in a purely unsupervised setting—without paired data or handcrafted priors. On RealSR and DRealSR benchmarks, TripleGAN achieves significant PSNR/SSIM gains over state-of-the-art methods. Reconstructed images exhibit sharp, natural textures with no oversmoothing artifacts, effectively bridging the synthetic-to-real degradation domain gap.
📝 Abstract
The training of real-world super-resolution reconstruction models heavily relies on datasets that reflect real-world degradation patterns. Extracting and modeling degradation patterns for super-resolution reconstruction using only real-world low-resolution (LR) images remains a challenging task. When synthesizing datasets to simulate real-world degradation, relying solely on degradation extraction methods fails to capture both blur and diverse noise characteristics across varying LR distributions, as well as more implicit degradations such as color gamut shifts. Conversely, domain translation alone cannot accurately approximate real-world blur characteristics due to the significant degradation domain gap between synthetic and real data. To address these challenges, we propose a novel TripleGAN framework comprising two strategically designed components: The FirstGAN primarily focuses on narrowing the domain gap in blur characteristics, while the SecondGAN performs domain-specific translation to approximate target-domain blur properties and learn additional degradation patterns. The ThirdGAN is trained on pseudo-real data generated by the FirstGAN and SecondGAN to reconstruct real-world LR images. Extensive experiments on the RealSR and DRealSR datasets demonstrate that our method exhibits clear advantages in quantitative metrics while maintaining sharp reconstructions without over-smoothing artifacts. The proposed framework effectively learns real-world degradation patterns from LR observations and synthesizes aligned datasets with corresponding degradation characteristics, thereby enabling the trained network to achieve superior performance in reconstructing high-quality SR images from real-world LR inputs.