# DIVD: Deblurring with Improved Video Diffusion Model

arXiv: https://arxiv.org/abs/2412.00773 · HTML: https://mlsanity.com/paper/2412.00773/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Inverse problems and image restoration, one of 14 directions in Diffusion models.
- Builds on 14 of its 70 references that are in the map.
- First posted on arXiv 1 Dec 2024.

## Placed in

- Diffusion models / [Inverse problems and image restoration](https://mlsanity.com/diffusion/inverse-problems-and-image-restoration/)

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Imagen Video: High Definition Video Generation with Diffusion Models](https://mlsanity.com/paper/2210.02303/) (2022)
- [Demystifying MMD GANs](https://mlsanity.com/paper/1801.01401/) (2018)
- [ModelScope Text-to-Video Technical Report](https://mlsanity.com/paper/2308.06571/) (2023)
- [Searching for Activation Functions](https://mlsanity.com/paper/1710.05941/) (2017)
- [A Recipe for Scaling up Text-to-Video Generation with Text-free Videos](https://mlsanity.com/paper/2312.15770/) (2023)
- [Revisiting Temporal Modeling for Video Super-resolution](https://mlsanity.com/paper/2008.05765/) (2020)
- [Flow-Guided Sparse Transformer for Video Deblurring](https://mlsanity.com/paper/2201.01893/) (2022)
- [FDAN: Flow-guided Deformable Alignment Network for Video Super-Resolution](https://mlsanity.com/paper/2105.05640/) (2021)

## Built on by (most cited first, 1 of 1)

- [RealVDeblur: One-Step Diffusion for Generalizable Real-World Video Deblurring](https://mlsanity.com/paper/2607.20628/) (2026)

## Abstract (arXiv)

Video deblurring presents a considerable challenge owing to the complexity of blur, which frequently results from a combination of camera shakes, and object motions. In the field of video deblurring, many previous works have primarily concentrated on distortion-based metrics, such as PSNR. However, this approach often results in a weak correlation with human perception and yields reconstructions that lack realism. Diffusion models and video diffusion models have respectively excelled in the fields of image and video generation, particularly achieving remarkable results in terms of image authenticity and realistic perception. However, due to the computational complexity and challenges inherent in adapting diffusion models, there is still uncertainty regarding the potential of video diffusion models in video deblurring tasks. To explore the viability of video diffusion models in the task of video deblurring, we introduce a diffusion model specifically for this purpose. In this field, leveraging highly correlated information between adjacent frames and addressing the challenge of temporal misalignment are crucial research directions. To tackle these challenges, many improvements based on the video diffusion model are introduced in this work. As a result, our model outperforms existing models and achieves state-of-the-art results on a range of perceptual metrics. Our model preserves a significant amount of detail in the images while maintaining competitive distortion metrics. Furthermore, to the best of our knowledge, this is the first time the diffusion model has been applied in video deblurring to overcome the limitations mentioned above.