Multi-Step Guided Diffusion for Image Restoration on Edge Devices: Toward Lightweight Perception in Embodied AI (2506.07286v1)

Abstract: Diffusion models have shown remarkable flexibility for solving inverse problems without task-specific retraining. However, existing approaches such as Manifold Preserving Guided Diffusion (MPGD) apply only a single gradient update per denoising step, limiting restoration fidelity and robustness, especially in embedded or out-of-distribution settings. In this work, we introduce a multistep optimization strategy within each denoising timestep, significantly enhancing image quality, perceptual accuracy, and generalization. Our experiments on super-resolution and Gaussian deblurring demonstrate that increasing the number of gradient updates per step improves LPIPS and PSNR with minimal latency overhead. Notably, we validate this approach on a Jetson Orin Nano using degraded ImageNet and a UAV dataset, showing that MPGD, originally trained on face datasets, generalizes effectively to natural and aerial scenes. Our findings highlight MPGD's potential as a lightweight, plug-and-play restoration module for real-time visual perception in embodied AI agents such as drones and mobile robots.

• The paper introduces a multi-step guided diffusion method that employs iterative gradient updates to enhance image restoration without task-specific retraining.
• The methodology leverages manifold preserving guided diffusion to address super-resolution and Gaussian deblurring, outperforming models like NAFNet and Uformer in real-time settings.
• Experimental validation on ImageNet and UAV123 using an NVIDIA Jetson Orin Nano demonstrates robust improvements in perceptual quality (LPIPS) and fidelity (PSNR) under edge constraints.

Multi-Step Guided Diffusion for Image Restoration on Edge Devices

The paper under discussion introduces a novel approach to image restoration with a focus on edge device feasibility, primarily targeting applications in embodied AI. The approach leverages Manifold Preserving Guided Diffusion (MPGD), reevaluated through a multi-step optimization lens to address two canonical inverse problems: super-resolution and Gaussian deblurring. By applying several gradient updates within each denoising step, the research investigates the trade-offs between image quality, diversity, and computational costs.

The research is motivated by the limitations of existing methods like Diffusion Posterior Sampling (DPS) and FreeDoM, which focus on task-specific objectives and often require extensive retraining. MPGD, as revisited in this work, operates without task-specific retraining and aims to generalize without being constrained to domains it was initially trained on. Leveraging principles observed in existing frameworks such as RePaint and Loss-Guided Diffusion (LGD), the research introduces multi-step conditioning that seeks to improve perceptual quality and pixel-level accuracy. The proposed method exhibits robustness, particularly in handling degraded or out-of-distribution images.

Experimental validation is conducted on both ImageNet and UAV123 datasets utilizing an NVIDIA Jetson Orin Nano platform, highlighting the approach's compliance with the power and compute constraints characteristic of edge computing environments. The results indicate significant improvements in perceptual quality metrics (like LPIPS) and fidelity measures (such as PSNR) upon increasing the number of guidance steps. Notably, MPGD, even though trained on face datasets, effectively restores images traditionally outside its trained domain, such as those from the UAV123 dataset.

The experiments demonstrate that MPGD's applicability enshrines itself in practical settings, such as aerial inspection scenarios where real-time processing is paramount. The research highlights its capability to outperform competing models like NAFNet and Uformer, particularly in perceptual metrics, maintaining real-time performance benchmarks.

This research contributes valuable insights into the efficacy of multi-step guided diffusion models for image restoration, specifically optimized for edge devices poised to operate under live conditions. It suggests that nuanced multi-step optimization strategies can effectively enhance generalization across varied domains even without retraining, offering a compelling plug-in solution for real-time image enhancement tasks in embodied AI.

The implications of this work are multifaceted. Practically, it presents a pathway towards integrating advanced image restoration capabilities into low-power, real-time applications, directly benefiting fields like autonomous navigation, UAV-based monitoring, and on-device AI. Theoretically, it challenges the perception of domain-specific diffusion modeling constraints, promoting broader applicability across heterogeneous datasets.

Future avenues appear promising, extending this approach to manifold perception challenges within embodied AI. Prospective development might explore adaptive optimization and lightweight adaptation strategies tailored to edge constraints. Additionally, expanding MPGD to accommodate multi-modal inputs could significantly enhance its utility, further entrenching this method as a cornerstone in real-time perception systems across diverse scenarios.