Residual Denoising Diffusion Models
Residual Denoising Diffusion Model (RDDM) introduces dual diffusion processes for unified image generation and restoration, leveraging residuals and noise with independent scheduling.
Key Findings
Methodology
RDDM employs a dual diffusion framework, decoupling the traditional single denoising process into residual diffusion and noise diffusion. Residual diffusion models directional shifts from the target image to degraded input, explicitly guiding restoration, while noise diffusion captures stochastic perturbations. Independent coefficient schedules for residuals and noise enable flexible control over certainty and diversity, facilitating multi-task learning. The architecture uses a simple UNet trained with L1 loss, supporting conditional inputs. A partially path-independent sampling strategy is proposed, ensuring robustness across different coefficient schedules. Coefficient transformations align RDDM with DDPM and DDIM, maintaining sampling consistency and enabling versatile applications.
Key Results
- On image restoration tasks like LOL low-light and RainDrop deraining, RDDM achieves PSNR of 30.72 and 31.96 respectively, surpassing existing methods with fewer parameters and training data. In image generation, on CelebA, FID drops to 23.25, demonstrating high-quality synthesis. The model's residual prediction excels in restoration, while noise prediction favors diversity in generation, validated through ablation studies.
- The independent coefficient scheduling allows flexible trade-offs, with transformations ensuring sampling consistency. The model performs robustly under various schedules, confirming theoretical guarantees. Results across multiple datasets show RDDM's adaptability and superior performance compared to SOTA baselines.
Significance
This work advances diffusion models by introducing a transparent, interpretable framework that unifies image generation and restoration. By explicitly modeling residuals, it addresses the black-box nature of prior methods, enabling better control and understanding. The minimal training complexity combined with strong multi-task performance paves the way for practical deployment in real-world applications, such as photo editing, medical imaging, and content creation. It also opens avenues for further theoretical exploration of multi-dimensional diffusion processes.
Technical Contribution
The paper proposes a dual diffusion process with independent residual and noise schedules, providing a new theoretical perspective on diffusion dynamics. It introduces a residual estimation mechanism, enabling the model to explicitly learn directional shifts, and develops a path-independent sampling strategy that enhances robustness. The framework supports conditional inputs with minimal training overhead, broadening the scope of diffusion models. The coefficient transformation theory ensures sampling consistency with existing methods, bridging the gap between interpretability and performance.
Novelty
This is the first work to incorporate residual diffusion explicitly into the diffusion framework, defining a dual process that separates directional residual shifts from stochastic perturbations. The partial path independence in sampling is a novel concept, allowing flexible coefficient scheduling without retraining. These innovations collectively enable a unified, interpretable, and versatile diffusion model capable of handling multiple image tasks with minimal architecture complexity.
Limitations
- Despite improvements, the model struggles with extremely degraded images where residual estimation becomes inaccurate, affecting restoration quality. The reliance on manual coefficient tuning limits scalability, and automatic scheduling remains an open challenge. Computational costs, while reduced, are still significant for high-resolution images, necessitating further optimization. The current framework primarily focuses on 2D images, requiring extension for videos or 3D data. Future work should address these issues for broader applicability.
Future Work
Future directions include developing adaptive coefficient scheduling mechanisms, integrating self-supervised learning for residual estimation, and extending the framework to temporal and volumetric data. Exploring more efficient architectures and training strategies will also be crucial for real-time applications. Additionally, theoretical analysis of multi-dimensional diffusion processes could deepen understanding, enabling even more flexible and robust models for diverse tasks.
AI Executive Summary
Diffusion models have revolutionized image synthesis, but their application to image restoration remains limited by interpretability and efficiency issues. Traditional models rely on a single denoising process, which often acts as a black box, making it difficult to explicitly control the direction of image recovery or generation. Moreover, these models are computationally intensive, requiring numerous sampling steps and complex schedules. To address these challenges, this paper introduces Residual Denoising Diffusion Models (RDDM), a novel framework that decomposes the diffusion process into two independent components: residual diffusion and noise diffusion.
The core innovation lies in modeling the residuals—representing the directional shift from the degraded input to the target image—explicitly, while simultaneously capturing stochastic perturbations through noise diffusion. This dual process allows the model to balance certainty and diversity, making it suitable for both image restoration and generation tasks within a unified architecture. The residual diffusion guides the reverse process explicitly, improving interpretability and efficiency. The authors design independent coefficient schedules for residuals and noise, enabling flexible, path-independent sampling that maintains consistency with existing diffusion methods like DDPM and DDIM.
Extensive experiments demonstrate the effectiveness of RDDM across multiple datasets. For instance, on low-light image enhancement, the model achieves a PSNR of 30.72, outperforming several state-of-the-art methods. In image generation, it attains a FID of 23.25 on CelebA, indicating high-quality synthesis. The model’s simplicity—training a single UNet with minimal data—combined with its versatility, marks a significant step forward. It opens new avenues for multi-task diffusion modeling, with potential applications in medical imaging, content creation, and beyond. Despite its strengths, the framework still faces challenges in extremely degraded scenarios and requires further automation of coefficient scheduling. Nonetheless, RDDM sets a promising foundation for interpretable, efficient, and unified diffusion-based image processing.
Deep Analysis
Background
近年来，扩散模型在图像生成领域取得突破，代表性工作如DDPM、DDIM等实现了高质量合成。随后，扩散模型逐步应用于图像修复、超分等任务，典型方法包括SR3、DPSNR等。传统扩散模型通过逐步去噪实现样本生成，但在图像修复中，逆向过程的非解释性和高计算成本成为瓶颈。近年来，条件扩散模型尝试引入条件输入改善修复效果，但仍依赖复杂调度和多次采样，限制了实际应用的效率与解释性。现有研究多关注模型性能，缺乏对多任务统一理解的探索，且模型复杂度较高，难以实现端到端的简洁训练。
Core Problem
核心问题在于扩散模型在图像修复中的非解释性和任务多样性不足。单一去噪过程难以同时兼顾生成多样性与修复的确定性，导致模型在不同任务间表现不一致。现有方法多依赖复杂调度和条件输入，训练成本高，泛化能力有限。如何设计一种既具解释性又能统一多任务的扩散框架，成为亟待解决的难题。
Innovation
提出残差去噪扩散模型（RDDM），引入双重扩散机制，明确残差与噪声的角色。残差扩散导向目标图像的方向性变化，增强模型的可解释性；噪声扩散模拟随机扰动，丰富多样性。设计独立调度系数，实现路径无关采样，提升模型鲁棒性。模型架构简洁，训练仅用L1损失，兼容多任务条件输入，突破了传统扩散模型的局限。
Methodology
- �� 定义双重扩散过程：残差扩散控制目标图像与降质图像的方向性变化，噪声扩散模拟随机扰动。• 设计两个独立系数调度αt和βt，分别调控残差与噪声的扩散速度。• 训练中，模型同时预测残差与噪声，使用L1损失优化。• 采样阶段，利用系数变换确保与DDPM、DDIM一致，支持路径无关的采样策略。• 采用UNet架构，条件输入支持多任务，模型训练简单高效。• 引入部分路径无关的调度策略，提高模型鲁棒性和泛化能力。
Experiments
在CelebA、LOL、雨滴去除等多个公开数据集上进行验证。采用FID、PSNR、SSIM等指标评估生成与修复性能。调研不同系数调度对性能的影响，验证路径无关采样的有效性。与SOTA方法如SR3、InDI等对比，展示优越性能。通过消融实验分析残差与噪声预测的贡献，验证模型的多任务能力。
Results
模型在LOL低光照修复中，PSNR达30.72，优于多数对比方法；在CelebA生成中，FID仅为23.25，表现优异。残差预测在修复任务中优于噪声预测，反之生成任务中噪声预测表现更佳。路径无关采样策略确保模型在不同系数调度下稳定性，验证了理论的正确性。
Applications
模型适用于图像修复、生成、去噪、超分等多场景，特别适合需要高效率和高解释性的工业应用。条件输入支持多任务联合训练，简化模型部署流程。未来可扩展到视频、三维重建等多模态任务，推动智能影像处理产业升级。
Limitations & Outlook
模型在极端降质环境下仍存在一定修复难度，残差估计误差影响效果。系数调度参数需依赖经验调优，自动调节机制尚未完善。高复杂度的模型训练对计算资源要求较高，未来需优化算法以提升效率。
Plain Language Accessible to non-experts
想象你在厨房里做菜，食材代表图像信息。传统的做法是按照食谱一步步加入调料，逐渐变得复杂。而这个新方法像是你提前准备好所有调料（残差和噪声），然后根据需要，快速调配出美味佳肴。残差就像是你知道菜的味道方向，帮你把菜做得更准；噪声像是偶尔加入的调料，让菜更丰富多彩。通过合理调节这两部分，你可以做出既好看又好吃的菜，不仅能修复受损的菜肴，还能创造出新菜式。这就像是用一种聪明的厨艺技巧，让厨房变得更高效、更有趣。
ELI14 Explained like you're 14
想象你在玩拼图游戏，有一块块碎片（图像信息）需要拼成完整的图片。以前的方法就像是把所有碎片都倒在桌子上，然后随机拼，可能拼得快但不一定漂亮。现在，这个新方法像是你提前知道图片的轮廓（残差），知道哪些碎片应该放在哪个位置（方向性），同时还加入一些随机的元素（噪声）让拼图更丰富。你可以先把轮廓拼好，再填充细节，最后调整颜色，让拼图既快又漂亮。这就像用一种聪明的拼图技巧，让你既能修复破碎的图片，也能创造出新颖的画面。
Abstract
We propose residual denoising diffusion models (RDDM), a novel dual diffusion process that decouples the traditional single denoising diffusion process into residual diffusion and noise diffusion. This dual diffusion framework expands the denoising-based diffusion models, initially uninterpretable for image restoration, into a unified and interpretable model for both image generation and restoration by introducing residuals. Specifically, our residual diffusion represents directional diffusion from the target image to the degraded input image and explicitly guides the reverse generation process for image restoration, while noise diffusion represents random perturbations in the diffusion process. The residual prioritizes certainty, while the noise emphasizes diversity, enabling RDDM to effectively unify tasks with varying certainty or diversity requirements, such as image generation and restoration. We demonstrate that our sampling process is consistent with that of DDPM and DDIM through coefficient transformation, and propose a partially path-independent generation process to better understand the reverse process. Notably, our RDDM enables a generic UNet, trained with only an L1 loss and a batch size of 1, to compete with state-of-the-art image restoration methods. We provide code and pre-trained models to encourage further exploration, application, and development of our innovative framework (https://github.com/nachifur/RDDM).