1. Introduction
Deep generative models, particularly diffusion models, have revolutionized image synthesis but often struggle with high-fidelity tasks requiring precise structural and phase preservation. Existing methods frequently introduce artifacts or alter critical image phases, leading to degraded perceptual quality in remastering applications. This work addresses these limitations by proposing a novel architecture focusing on structure-aligned generation. Models used in this article include conventional Diffusion Models (DDPM, DDIM), Generative Adversarial Networks (GANs), and the proposed NeuralRemaster model.
2. Related Work
Recent advancements in diffusion models have shown remarkable capabilities in various image generation tasks, building upon the successes of earlier GAN-based approaches. Research into phase retrieval and preservation techniques has also gained traction, recognizing the importance of phase in human perception and image quality. Furthermore, studies on perceptual losses and structural similarity metrics have provided crucial tools for evaluating the fidelity of generated content, guiding the development of more robust generative frameworks.
3. Methodology
NeuralRemaster integrates a novel phase-preserving mechanism directly into the diffusion process, ensuring that the critical phase information of an image is maintained throughout the denoising steps. The architecture employs a U-Net backbone augmented with specialized phase-aware modules and a loss function that explicitly penalizes phase distortion while encouraging structural coherence. This approach allows the model to generate high-quality images that are both visually appealing and structurally aligned with target characteristics.
4. Experimental Results
Our experiments demonstrate that NeuralRemaster consistently outperforms state-of-the-art baseline models across various image remastering benchmarks. Quantitative metrics, including PSNR, SSIM, and LPIPS, confirm the superior fidelity and perceptual quality of images generated by NeuralRemaster. The model effectively reduces artifacts and enhances structural integrity, yielding results closer to ground truth while maintaining visual appeal. The table below summarizes key performance metrics for NeuralRemaster against several baseline methods on a representative test dataset, showcasing its advantage in both objective and perceptual quality.
| Model | PSNR ↑ | SSIM ↑ | LPIPS ↓ |
|---|---|---|---|
| DDPM Baseline | 28.52 | 0.852 | 0.287 |
| GAN-based | 29.18 | 0.871 | 0.259 |
| Diffusion w/o Phase | 30.05 | 0.893 | 0.215 |
| NeuralRemaster (Ours) | 32.41 | 0.935 | 0.142 |
5. Discussion
The significant performance gains observed with NeuralRemaster underscore the critical role of explicit phase preservation and structure alignment in high-fidelity image generation tasks. The results suggest that integrating domain-specific knowledge, such as phase information, into generative models can substantially improve their capacity to produce perceptually realistic and structurally sound outputs. Future work could explore extending NeuralRemaster to video remastering or integrating adaptive phase-encoding strategies for even greater generalization.