- The paper presents a novel transformer-based model (BiT) that employs multi-scale residual blocks, dual-end temporal supervision, and symmetric ensembling for effective blur interpolation.
- It achieves significant performance gains in PSNR and SSIM on synthetic (Adobe240) and real-world (RBI) datasets, outperforming traditional optical flow methods.
- Its efficient architecture and the introduction of the RBI dataset pave the way for practical applications in video enhancement and slow-motion generation.
The paper "Blur Interpolation Transformer for Real-World Motion from Blur" addresses the complex problem of recovering motion from blur, focusing on joint deblurring and interpolation, or blur temporal super-resolution. The main challenges identified in this area are the limited visual quality of current methods on synthetic datasets and poor generalization to real-world data. To tackle these issues, the authors introduce a novel blur interpolation transformer (BiT) that leverages the temporal correlation present in blurred data. This work incorporates multiple innovative approaches, including the use of multi-scale residual Swin transformer blocks, dual-end temporal supervision, and temporally symmetric ensembling strategies, to enhance the rendering of time-varying motion features.
Methodology
The core contribution of this paper is the development of BiT, a transformer-based model designed for arbitrary time motion reconstruction from blurred inputs. BiT introduces a multi-scale residual Swin transformer block that processes features at various scales, enhancing its ability to handle complex motions without prior motion range information. The model's dual-end temporal supervision (DTS) provides boundary anchors to facilitate feature learning for different time points, effectively supporting arbitrary motion rendering in a latent temporal space. Temporally symmetric ensembling (TSE) improves generalization and robustness by ensuring consistency in predictions from temporally forward and inversed blurred inputs.
These strategies collectively underpin BiT's significant performance gains over state-of-the-art methods on synthetic datasets like Adobe240 and new real-world datasets, while also maintaining computational efficiency. The model is trained using L1 loss with strategic temporal supervision, enabling high accuracy across varying conditions.
Real-World Dataset
To address the lack of real-world training data, the authors constructed a hybrid camera system, leading to the collection of the first-ever real-world dataset for blur interpolation, named RBI. This dataset, consisting of time-aligned low-frame-rate blurred and high-frame-rate sharp video pairs, offers an authentic benchmark for evaluating blur interpolation in practical scenarios. The dataset's significance is underscored by the authors' observation that synthetic datasets often introduce unrealistic motion blur artifacts, which can hamper model generalization to real-world conditions.
Results and Implications
BiT demonstrates superior performance quantitatively and qualitatively, as evidenced by improvements in PSNR and SSIM scores across synthetic and real datasets. The model's capability to accurately produce sharp frames from blurred inputs without relying on optical flow-based warping is notable. Furthermore, BiT's efficient architecture allows it to generate multiple inferences in reduced runtime compared to previous methods.
The implications of this work are substantial for applications in video enhancement, slow-motion generation, and dynamic scene understanding, offering robust tools for handling real-world motion blur. The insights from this paper could pave the way for further research into more adaptive and generalizable vision models, potentially extending beyond interpolation to more complex scene reconstruction tasks.
Future Directions
The authors identify potential improvements in dealing with extremely fast motions and adapting models across diverse real-world scenarios with different devices and exposure parameters. Future research could explore better continuous supervision strategies and expanding real-world datasets. Additionally, the reverse process of learning to synthesize realistic blur from sharp videos presents an intriguing avenue for further exploration, with possible applications in simulating natural motion blur for augmented reality and computer graphics.
In conclusion, this work marks a significant advancement in blur interpolation methods, offering a promising trajectory for further research and development in computer vision and image processing.