Residual Denoising Diffusion Models: Unified Image Generation and Restoration

Core Concepts

Residual Denoising Diffusion Models (RDDM) propose a dual diffusion process to unify image generation and restoration by introducing residuals and noise diffusion.

Abstract

RDDM introduces a novel dual diffusion process that decouples residual diffusion and noise diffusion for image generation and restoration. The forward process involves degrading the target image to the degraded input image while injecting noise, resulting in a dual diffusion framework. The reverse generation process estimates residuals and noise injected in the forward process using two separate networks. Empirical research suggests using SM-N for tasks requiring diversity and SM-Res for tasks demanding certainty. An automatic objective selection algorithm is developed to choose between SM-N or SM-Res based on the task requirements. A partially path-independent generation process is proposed, allowing modifications to the diffusion speed curve without affecting image semantics.

Stats

DDPM [17]とDDIM [51]によるサンプリングプロセスは、係数変換を通じてRDDMに適合する。 ノイズの速度曲線を変更することで画像生成プロセスが失敗する可能性がある。

Quotes

"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 envision that our models can facilitate a unified and interpretable image-to-image distribution transformation methodology."

Key Insights Distilled From

Residual Denoising Diffusion Models

by Jiawei Liu,Q... at arxiv.org 03-25-2024

https://arxiv.org/pdf/2308.13712.pdf

Deeper Inquiries

How does the introduction of residuals in RDDM impact the interpretability of the denoising process

RDDM introduces residuals to represent directional diffusion from the target image to the degraded input image. This addition significantly impacts the interpretability of the denoising process by providing a clear indication of how information is diffused in a specific direction during restoration. Unlike traditional denoising processes that lack this directional guidance, RDDM with residuals offers a more intuitive and understandable approach to image restoration. Residuals prioritize certainty in the diffusion process, making it easier to comprehend how the restoration is guided from the target image towards the degraded input.

What are the implications of decoupling residual diffusion from noise diffusion in image restoration

Decoupling residual diffusion from noise diffusion in image restoration has several implications for improving the overall effectiveness of the process: Directional Guidance: By separating residual and noise diffusion, RDDM can provide clearer directional guidance for restoring images. The residual diffusion represents a focused shift towards certainty, while noise diffusion adds diversity. Enhanced Control: Decoupling allows for independent control over each aspect of the restoration process, enabling better fine-tuning based on specific requirements for certainty or diversity. Improved Adaptability: The ability to adjust residual and noise independently enhances adaptability across different tasks with varying complexity levels or quality demands. Interpretability: Separating these two types of diffusions makes it easier to interpret and understand how each component contributes to image restoration outcomes.

How can partially path-independent generation processes improve robustness in image restoration tasks

Partially path-independent generation processes offer improved robustness in image restoration tasks by ensuring stability and consistency even when modifications are made to certain aspects of the generation process: Resilience against Disturbances: A partially path-independent approach ensures that disturbances within a certain range do not disrupt or compromise generation results. Consistent Semantic Transitions: Modifications can be made without affecting semantic transitions in generated images, leading to stable outputs despite changes in parameters or schedules. Reduced Variance Impact: By maintaining independence between different components like residuals and noise during generation, variations introduced through modifications have minimal impact on final results. This enhanced robustness leads to more reliable performance across various scenarios and settings in image restoration tasks.