# DAVANet: Stereo Deblurring with View Aggregation

arXiv: https://arxiv.org/abs/1904.05065 · HTML: https://mlsanity.com/paper/1904.05065/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2019.

## Built on by (most cited first, 30 of 37)

- [LEDNet: Joint Low-light Enhancement and Deblurring in the Dark](https://mlsanity.com/paper/2202.03373/) (2022)
- [Deep Image Deblurring: A Survey](https://mlsanity.com/paper/2201.10700/) (2022)
- [Realistic Blur Synthesis for Learning Image Deblurring](https://mlsanity.com/paper/2202.08771/) (2022)
- [NTIRE 2021 Challenge on Image Deblurring](https://mlsanity.com/paper/2104.14854/) (2021)
- [MC-Blur: A Comprehensive Benchmark for Image Deblurring](https://mlsanity.com/paper/2112.00234/) (2021)
- [Deep Recurrent Neural Network with Multi-scale Bi-directional Propagation for Video Deblurring](https://mlsanity.com/paper/2112.05150/) (2021)
- [Real-World Deep Local Motion Deblurring](https://mlsanity.com/paper/2204.08179/) (2022)
- [Deblur4DGS: 4D Gaussian Splatting from Blurry Monocular Video](https://mlsanity.com/paper/2412.06424/) (2024)
- [Beyond Monocular Deraining: Parallel Stereo Deraining Network Via Semantic Prior](https://mlsanity.com/paper/2105.03830/) (2021)
- [Self-Supervised Image Restoration with Blurry and Noisy Pairs](https://mlsanity.com/paper/2211.07317/) (2022)
- [LanP: Rethinking the Impact of Language Priors in Large Vision-Language Models](https://mlsanity.com/paper/2502.12359/) (2025)
- [Recovering 3D Hand Mesh Sequence from a Single Blurry Image: A New Dataset and Temporal Unfolding](https://mlsanity.com/paper/2303.15417/) (2023)
- [MoBluRF: Motion Deblurring Neural Radiance Fields for Blurry Monocular Video](https://mlsanity.com/paper/2312.13528/) (2023)
- [Exploiting Deblurring Networks for Radiance Fields](https://mlsanity.com/paper/2502.14454/) (2025)
- [Revealing the preference for correcting separated aberrations in joint optic-image design](https://mlsanity.com/paper/2309.04342/) (2023)
- [Stereo Waterdrop Removal with Row-wise Dilated Attention](https://mlsanity.com/paper/2108.03457/) (2021)
- [Learning Dual-Pixel Alignment for Defocus Deblurring](https://mlsanity.com/paper/2204.12105/) (2022)
- [Self-Supervised Intensity-Event Stereo Matching](https://mlsanity.com/paper/2211.00509/) (2022)
- [Decoupled Cross-Scale Cross-View Interaction for Stereo Image Enhancement in The Dark](https://mlsanity.com/paper/2211.00859/) (2022)
- [Learning Parallax for Stereo Event-based Motion Deblurring](https://mlsanity.com/paper/2309.09513/) (2023)
- [Breaking the Vicious Cycle: Coherent 3D Gaussian Splatting from Sparse and Motion-Blurred Views](https://mlsanity.com/paper/2512.10369/) (2025)
- [WAFT-Stereo: Warping-Alone Field Transforms for Stereo Matching](https://mlsanity.com/paper/2603.24836/) (2026)
- [Stereo Image Rain Removal via Dual-View Mutual Attention](https://mlsanity.com/paper/2211.10104/) (2022)
- [Depth-Aware Image Compositing Model for Parallax Camera Motion Blur](https://mlsanity.com/paper/2303.09334/) (2023)
- [Reference-based Motion Blur Removal: Learning to Utilize Sharpness in the Reference Image](https://mlsanity.com/paper/2307.02875/) (2023)
- [SGDFormer: One-stage Transformer-based Architecture for Cross-Spectral Stereo Image Guided Denoising](https://mlsanity.com/paper/2404.00349/) (2024)
- [Deblur-Avatar: Animatable Avatars from Motion-Blurred Monocular Videos](https://mlsanity.com/paper/2501.13335/) (2025)
- [Video Demoireing using Focused-Defocused Dual-Camera System](https://mlsanity.com/paper/2508.03449/) (2025)
- [Moment-Reenacting: Inverse Motion Degradation with Cross-shutter Guidance](https://mlsanity.com/paper/2605.22423/) (2026)
- [H2-Stereo: High-Speed, High-Resolution Stereoscopic Video System](https://mlsanity.com/paper/2208.02436/) (2022)

## Abstract (arXiv)

Nowadays stereo cameras are more commonly adopted in emerging devices such as dual-lens smartphones and unmanned aerial vehicles. However, they also suffer from blurry images in dynamic scenes which leads to visual discomfort and hampers further image processing. Previous works have succeeded in monocular deblurring, yet there are few studies on deblurring for stereoscopic images. By exploiting the two-view nature of stereo images, we propose a novel stereo image deblurring network with Depth Awareness and View Aggregation, named DAVANet. In our proposed network, 3D scene cues from the depth and varying information from two views are incorporated, which help to remove complex spatially-varying blur in dynamic scenes. Specifically, with our proposed fusion network, we integrate the bidirectional disparities estimation and deblurring into a unified framework. Moreover, we present a large-scale multi-scene dataset for stereo deblurring, containing 20,637 blurry-sharp stereo image pairs from 135 diverse sequences and their corresponding bidirectional disparities. The experimental results on our dataset demonstrate that DAVANet outperforms state-of-the-art methods in terms of accuracy, speed, and model size.