Diffusion Models in Low-Level Vision: A Survey (2406.11138v1)

Abstract: Deep generative models have garnered significant attention in low-level vision tasks due to their generative capabilities. Among them, diffusion model-based solutions, characterized by a forward diffusion process and a reverse denoising process, have emerged as widely acclaimed for their ability to produce samples of superior quality and diversity. This ensures the generation of visually compelling results with intricate texture information. Despite their remarkable success, a noticeable gap exists in a comprehensive survey that amalgamates these pioneering diffusion model-based works and organizes the corresponding threads. This paper proposes the comprehensive review of diffusion model-based techniques. We present three generic diffusion modeling frameworks and explore their correlations with other deep generative models, establishing the theoretical foundation. Following this, we introduce a multi-perspective categorization of diffusion models, considering both the underlying framework and the target task. Additionally, we summarize extended diffusion models applied in other tasks, including medical, remote sensing, and video scenarios. Moreover, we provide an overview of commonly used benchmarks and evaluation metrics. We conduct a thorough evaluation, encompassing both performance and efficiency, of diffusion model-based techniques in three prominent tasks. Finally, we elucidate the limitations of current diffusion models and propose seven intriguing directions for future research. This comprehensive examination aims to facilitate a profound understanding of the landscape surrounding denoising diffusion models in the context of low-level vision tasks. A curated list of diffusion model-based techniques in over 20 low-level vision tasks can be found at https://github.com/ChunmingHe/awesome-diffusion-models-in-low-level-vision.

Analysis of Diffusion Models in Low-Level Vision: A Survey

The paper "Diffusion Models in Low-Level Vision: A Survey" provides a comprehensive examination of the implementation and impact of denoising diffusion models in low-level vision tasks. The authors, Chunming He and colleagues, articulate the intricate details of these models, their application scope, and the potential future directions for this research trajectory. This essay elucidates the key points discussed in the survey and provides an expert perspective on the implications and future prospects of diffusion models within low-level vision tasks.

The survey begins by detailing the theoretical underpinnings of diffusion models, with a focus on three main frameworks: Denoising Diffusion Probabilistic Models (DDPMs), Noise-Conditioned Score Networks (NCSNs), and Stochastic Differential Equations (SDEs). These frameworks leverage forward and reverse processes to perturb and subsequently denoise data, ensuring that the outcome is a high-fidelity, high-quality image. This theoretical foundation is crucial for comprehending the subsequent practical applications and comparisons with other deep generative models such as GANs, VAEs, and normalizing flows.

Diffusion Models for Natural Image Processing

The survey categorizes the application of diffusion models into various low-level vision tasks, which are essential for enhancing low-quality images. These tasks include general-purpose image restoration, super-resolution, inpainting, deblurring, dehazing, low-light image enhancement, and image fusion.

1. General-Purpose Image Restoration: The authors delve into both supervised and zero-shot methods that harness pre-trained diffusion models to solve inverse problems, such as super-resolution and inpainting. Notable methods like DDRM and CDDB have shown remarkable performance, leveraging plug-and-play techniques and score-based frameworks for effective image restoration.
2. Super-Resolution (SR): Several diffusion model-based super-resolution methods, such as SRDiff, CDM, and IDM, are discussed. These methods have demonstrated the ability to generate highly detailed images from low-resolution inputs, addressing issues of over-smoothing and artifacts inherent in traditional SR methods.
3. Inpainting: Techniques like RePaint and BrushNet utilize diffusion models to handle large missing regions in images. These methods have proven effective in generating plausible inpainted results, maintaining visual coherence and detail.
4. Deblurring: The survey highlights methods such as DSR and MSGD, which have employed diffusion models to tackle motion blur, thereby restoring sharp and clear images. These methods leverage multi-scale structural guidance and hierarchical integration for enhanced performance.
5. Dehazing, Deraining, and Desnowing: Diffusion models have also shown efficacy in weather-specific restoration tasks. For instance, WeatherDiffusion and Refusion address complex degradations such as haze, rain, and snow, demonstrating the versatility of diffusion models.
6. Low-Light Image Enhancement: Techniques like LLIE and PyDiff leverage diffusion models to enhance images captured in low-light conditions, significantly improving the visibility and detail of these images.
7. Image Fusion: Diffusion models have been applied to tasks such as infrared and visible image fusion, as seen in methods like Dif-Fusion and DDFM. These methods seamlessly integrate data from multiple sources, ensuring high-quality fused outputs.

Extended Applications of Diffusion Models

Beyond natural image processing, diffusion models have been extended to other specialized domains, including medical imaging, remote sensing, and video analysis.

1. Medical Image Processing: Diffusion models have been applied to tasks like MRI and CT reconstruction, denoising, and image translation. For instance, DOLCE and ScoreMRI leverage diffusion models to enhance medical images, demonstrating significant improvements in image quality for diagnostic purposes.
2. Remote Sensing Data: The versatility of diffusion models extends to remote sensing tasks such as super-resolution, cloud removal, and multi-modal fusion. Methods like Cloud Removal and DDS2M have addressed specific challenges in hyperspectral imaging and SAR data, showcasing the adaptability of diffusion models in varied scenarios.
3. Video Processing: In video tasks, diffusion models have been employed for frame prediction, interpolation, super-resolution, and restoration. Techniques like SATeCo and Diff-TSC have demonstrated the capacity of diffusion models to handle temporal consistency and generate high-quality video frames.

Implications and Future Directions

The survey identifies several limitations and proposes future research directions to enhance the capabilities and applications of diffusion models.

1. Mitigating Limitations: Reducing computational overhead and compressing model size are critical for real-time applications. Techniques such as non-Markov Chain modeling and knowledge distillation are promising approaches to improve sampling efficiency and reduce inference time.
2. Amalgamating Strengths: Enhancing perception-distortion trade-offs and designing downstream task-friendly models are essential for real-world applicability. Hybrid models that combine diffusion models with CNNs and transformers are a promising direction for achieving better perceptual and distortion-based performance.
3. Tackling Data Challenges: Addressing data-hungry fields through pseudo image pair generation and interactive guidance priors are crucial for improving generalizability and enhancing training.

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In conclusion, the survey by He et al. thoroughly investigates the integration and implications of diffusion models in low-level vision tasks. The profound theoretical insights, coupled with practical applications and future research directions, present a comprehensive overview of the current landscape and potential advancements in this field. The survey is a valuable resource for researchers aiming to explore the intersection of diffusion models and low-level vision tasks, offering a solid foundation for future studies and innovations.