Burst Super-Resolution with Diffusion Models for Improving Perceptual Quality

Core Concepts

The core message of this paper is to propose a burst super-resolution method that employs diffusion models to improve the perceptual quality of the super-resolved images, by appropriately conditioning the diffusion model with features extracted from the input burst low-resolution images and starting the reverse process from an intermediate step of the diffusion model.

Abstract

The paper proposes a burst super-resolution method called Burst Super-Resolution with Diffusion Model (BSRD) that aims to improve the perceptual quality of the super-resolved images. The key aspects of the proposed method are: Feature Extraction and Alignment: The paper borrows the feature extraction and alignment modules from the state-of-the-art deterministic burst super-resolution method Burstormer. These modules extract and align features from the input burst low-resolution (LR) images. Burst Feature Conditioning for Diffusion Models: The extracted and aligned features are used to condition the diffusion model, which is employed for the probabilistic reconstruction of the super-resolved image. The conditioning is achieved through Spatial Feature Transformation (SFT) to properly format the features for the U-Net architecture of the diffusion model's reverse process. Efficient and High-quality SR Reconstruction by the Reverse Process from Intermediate Steps: Instead of starting the reverse process from a random noise, the paper proposes to start from an intermediate step of the diffusion model, using the initial burst super-resolved image obtained from a deterministic burst SR method. This reduces the computational cost and allows the reverse process to focus on reconstructing fine details. The experimental results on the SyntheticBurst and BurstSR datasets demonstrate that the proposed BSRD method can significantly improve the perceptual quality metrics like LPIPS and FID compared to the deterministic burst SR methods, while maintaining reasonable performance on distortion-based metrics like PSNR and SSIM.

Stats

The paper reports the following key figures: On the SyntheticBurst dataset, BSRD improves LPIPS by up to 8.6% and FID by up to 36% compared to the original Burstormer method. On the BurstSR dataset, BSRD improves LPIPS by 5.8% and FID by 43% compared to the BIPNet method.

Quotes

"Our experimental results demonstrate that our method can improve the scores of the perceptual quality metrics." "To avoid this problem, this paper aims to improve the perceptual quality of the SR image by probabilistic modeling." "Since such blurry images are perceptually degraded, we aim to reconstruct the sharp high-fidelity boundaries."

Key Insights Distilled From

Burst Super-Resolution with Diffusion Models for Improving Perceptual Quality

by Kyotaro Toko... at arxiv.org 03-29-2024

https://arxiv.org/pdf/2403.19428.pdf

Deeper Inquiries

How can the proposed BSRD method be extended to handle real-world burst images with more complex degradations beyond just translation and rotation

To extend the proposed BSRD method to handle real-world burst images with more complex degradations beyond just translation and rotation, several enhancements can be considered. Firstly, incorporating advanced alignment techniques that can handle more intricate transformations such as scaling, perspective changes, and non-linear distortions would be beneficial. Utilizing deformable alignment mechanisms or attention mechanisms that can adapt to various types of displacements can improve the alignment accuracy for complex real-world scenarios. Additionally, integrating robust feature extraction methods that can capture a wider range of variations in burst images, including occlusions, lighting changes, and background clutter, would enhance the model's ability to reconstruct high-quality images from challenging inputs. Moreover, incorporating domain-specific knowledge or priors into the diffusion model to account for specific characteristics of real-world burst images can further improve the model's performance in handling complex degradations.

What are the potential limitations of the diffusion model-based approach compared to other probabilistic models, and how can they be addressed

While the diffusion model-based approach offers several advantages for image enhancement and restoration tasks, it also has some potential limitations compared to other probabilistic models. One limitation is the computational complexity associated with training and inference in diffusion models, especially when dealing with high-resolution images or long diffusion chains. This can lead to increased training times and resource requirements, making it less practical for real-time applications or large-scale datasets. To address this limitation, techniques such as model distillation, network pruning, or leveraging parallel computing architectures can be explored to optimize the efficiency of diffusion models. Another limitation is the sensitivity of diffusion models to hyperparameters and training strategies, which can impact the quality of generated images. Fine-tuning hyperparameters, exploring different noise schedules, and incorporating regularization techniques can help mitigate this issue and improve the stability and robustness of the model.

Given the tradeoff between perceptual and distortion-based metrics, how can the BSRD method be further improved to achieve a better balance between these two aspects of image quality

To achieve a better balance between perceptual and distortion-based metrics in the BSRD method, several strategies can be implemented. One approach is to incorporate multi-objective optimization techniques that simultaneously consider both perceptual quality and traditional image quality metrics during training. By formulating a joint loss function that combines perceptual loss functions like LPIPS with distortion-based metrics like PSNR, the model can learn to optimize both aspects of image quality effectively. Additionally, exploring advanced regularization methods, such as adversarial training or perceptual loss functions based on pre-trained deep neural networks, can help enhance the perceptual quality of the generated images while maintaining low distortion levels. Fine-tuning the weighting of different loss components based on the specific requirements of the application can further tailor the model to achieve the desired balance between perceptual and distortion-based image quality metrics.