Efficient Diffusion-based Method for Ghost-free High Dynamic Range Imaging

核心概念

The proposed LF-Diff method leverages the powerful distribution estimation capability of diffusion models to efficiently generate compact low-frequency priors, which are then integrated into a regression-based network to reconstruct high-quality HDR images with reduced ghosting artifacts.

要約

The paper introduces the LF-Diff framework for ghost-free HDR imaging, which consists of two training stages:

Stage 1 - Pretraining LF-Diff:

• The Low-frequency Prior Extraction Network (LPENet) is trained to extract accurate low-frequency prior representations (LPR) from ground truth HDR images.
• The Dynamic HDR Reconstruction Network (DHRNet) is designed to effectively utilize the LPR features extracted by LPENet to reconstruct high-quality HDR images.
• The LPENet and DHRNet are jointly optimized in this stage.

Stage 2 - Diffusion Model Training:

• A lightweight diffusion model is trained to efficiently predict the LPR directly from the input LDR images.
• The diffusion model and DHRNet are jointly optimized to generate the final HDR images.

The key innovations of LF-Diff include:

1. Leveraging diffusion models to generate compact low-frequency priors, which are then integrated into the HDR reconstruction process.
2. Proposing the Prior Integration Module (PIM) and Feature Refinement Module (FRM) in DHRNet to effectively exploit the low-frequency priors.
3. Achieving state-of-the-art performance on benchmark datasets while being significantly more computationally efficient compared to previous diffusion-based methods.

Extensive experiments demonstrate that LF-Diff outperforms various state-of-the-art HDR imaging methods in terms of both quantitative metrics and visual quality, while being 10x faster than previous diffusion-based approaches.

統計

Our method performs favorably against several state-of-the-art methods and is 10× faster than previous diffusion-model-based methods.
LF-Diff achieves a PSNR-L of 42.59 dB on Kalantari's dataset, outperforming the previous diffusion-based method DiffHDR by 0.86 dB.
On Hu's dataset, LF-Diff achieves a PSNR-L of 52.10 dB, surpassing the runner-up method HyHDR by 0.19 dB.

引用

"Recovering ghost-free High Dynamic Range (HDR) images from multiple Low Dynamic Range (LDR) images becomes challenging when the LDR images exhibit saturation and significant motion."
"Diffusion Models (DMs) have been introduced in HDR imaging field, demonstrating promising performance, particularly in achieving visually perceptible results compared to previous DNN-based methods."
"DMs require extensive iterations with large models to estimate entire images, resulting in inefficiency that hinders their practical application."

抽出されたキーインサイト

Generating Content for HDR Deghosting from Frequency View

by Tao Hu,Qings... 場所 arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.00849.pdf

深掘り質問

How can the proposed LF-Diff framework be extended to other low-level vision tasks beyond HDR imaging, such as image denoising or super-resolution

The LF-Diff framework can be extended to other low-level vision tasks beyond HDR imaging by adapting the model architecture and training strategy to suit the specific requirements of the new task. For image denoising, the LPENet can be trained to extract low-frequency features that are indicative of noise patterns in images. These features can then be used to guide a denoising network in removing noise while preserving image details. The denoising network can be optimized to learn the mapping between noisy and clean images, similar to the regression-based model in LF-Diff. Additionally, the diffusion model can be utilized to estimate noise distributions and refine the denoised images iteratively.
For super-resolution tasks, the LPENet can extract low-frequency priors that capture the global structure of high-resolution images. These priors can guide a super-resolution network in enhancing image details and increasing spatial resolution. The integration of the diffusion model can help in refining the super-resolved images by estimating high-frequency details and textures.
In both cases, the joint training strategy used in LF-Diff can be applied to optimize the denoising or super-resolution networks along with the diffusion model. This approach ensures that the networks learn to effectively utilize the low-frequency priors for improved performance in the respective tasks.

What are the potential limitations of using diffusion models in HDR imaging, and how can they be addressed in future research

One potential limitation of using diffusion models in HDR imaging is the computational complexity and time-consuming nature of the iterative sampling process. Diffusion models require multiple iterations to estimate the distribution of pixel values, which can be computationally intensive, especially for high-resolution images. This can lead to longer inference times and increased resource requirements, making real-time applications challenging.
To address this limitation, future research can focus on developing more efficient sampling strategies or approximations that reduce the number of iterations needed for accurate estimation. Techniques such as adaptive sampling schedules or hierarchical sampling approaches can help speed up the diffusion process without compromising the quality of the reconstructed HDR images. Additionally, exploring parallelization techniques and hardware acceleration can further optimize the computational efficiency of diffusion models in HDR imaging tasks.
Another limitation is the potential for diffusion models to struggle with capturing fine details and textures in HDR images, especially in regions with high-frequency content. Future research can investigate the integration of additional modules or priors that focus on capturing and enhancing high-frequency information to improve the overall visual quality of reconstructed HDR images.

Given the success of the low-frequency prior in LF-Diff, how can the integration of multi-scale priors further enhance the performance of diffusion-based HDR reconstruction

The integration of multi-scale priors in diffusion-based HDR reconstruction can further enhance the performance by capturing a broader range of frequency information and improving the overall fidelity of the reconstructed images. By incorporating priors at different scales, the model can effectively leverage both global context and local details to generate more visually appealing HDR results.
One approach to integrating multi-scale priors is to design a hierarchical LPENet that extracts low-frequency features at different resolutions. These features can then be used to guide the reconstruction process at multiple scales, allowing the model to capture both global structures and fine details in the HDR images. The diffusion model can be adapted to estimate distributions at different scales, enabling the refinement of image details across various frequency bands.
Additionally, the DHRNet can be modified to incorporate multi-scale feature fusion mechanisms that combine information from different levels of the network. Techniques such as multi-resolution attention mechanisms or feature pyramids can help integrate information from different scales effectively, leading to more comprehensive and accurate HDR reconstructions. By leveraging multi-scale priors, the LF-Diff framework can achieve superior performance in handling complex scenes with diverse frequency content.