From Motion Blur to Motion Flow: a Deep Learning Solution for Removing Heterogeneous Motion Blur (1612.02583v1)

Abstract: Removing pixel-wise heterogeneous motion blur is challenging due to the ill-posed nature of the problem. The predominant solution is to estimate the blur kernel by adding a prior, but the extensive literature on the subject indicates the difficulty in identifying a prior which is suitably informative, and general. Rather than imposing a prior based on theory, we propose instead to learn one from the data. Learning a prior over the latent image would require modeling all possible image content. The critical observation underpinning our approach is thus that learning the motion flow instead allows the model to focus on the cause of the blur, irrespective of the image content. This is a much easier learning task, but it also avoids the iterative process through which latent image priors are typically applied. Our approach directly estimates the motion flow from the blurred image through a fully-convolutional deep neural network (FCN) and recovers the unblurred image from the estimated motion flow. Our FCN is the first universal end-to-end mapping from the blurred image to the dense motion flow. To train the FCN, we simulate motion flows to generate synthetic blurred-image-motion-flow pairs thus avoiding the need for human labeling. Extensive experiments on challenging realistic blurred images demonstrate that the proposed method outperforms the state-of-the-art.

• The paper introduces a novel FCN that directly learns pixel-wise motion flow from blurred images, eliminating the need for explicit image priors.
• It leverages simulated motion flows from realistic camera and object movements to create a comprehensive training dataset, ensuring robustness across varied blur patterns.
• The method outperforms traditional iterative techniques in both runtime efficiency and deblurring quality, achieving higher PSNR and SSIM on synthetic benchmarks.

Overview of "From Motion Blur to Motion Flow: a Deep Learning Solution for Removing Heterogeneous Motion Blur"

The paper presents a novel and flexible approach to addressing the problem of heterogeneous motion blur in single image deblurring. The traditional techniques often assume spatially-uniform blur and rely heavily on imposing a prior on the latent (sharp) image, which may not adequately capture the complexities of real-world blurs that are spatially-varying and pixel-dependent. Instead, this paper introduces a fully-convolutional network (FCN) designed to learn pixel-wise motion flow directly from blurred images, eliminating the need for iterative processes that rely on heuristically-assumed priors.

Key Contributions

1. End-to-End Motion Flow Learning: The paper proposes the first universal FCN for estimating dense motion flow directly from blurred images. This network avoids reliance on explicit image priors by learning the deblurring process from data, focusing on estimating the motion that caused the blur rather than modeling the entire latent image content. The FCN is trained on synthetic blurred-image-motion-flow pairs, circumventing the burden of manual labeling.
2. Dataset Generation Using Motion Simulation: A key challenge in training neural networks for motion flow estimation is the lack of ground-truth data. This work addresses that by simulating realistic motion flows, drawing from known models of camera and object motion, to create a comprehensive training dataset. Samples feature diverse motion patterns, including translations and rotations, ensuring the model generalizes well across different blur scenarios.
3. Evaluation and Performance: Compared against methods such as those proposed by Xu and Jia, Sun et al., and Whyte et al., the paper demonstrates superior performance on synthetic datasets in terms of PSNR and SSIM metrics. The proposed method maintains a robust edge in handling diverse image contents and complex motion patterns. A notable feature of this approach is its computational efficiency, markedly outperforming iterative methods in terms of runtime.

Implications and Future Directions

The proposed method alleviates some inherent limitations of traditional deblurring methods by leveraging end-to-end learning of motion causes rather than effects. This shift from image priors to motion flow estimation could herald new directions in handling complex deblurring tasks without sophisticated preprocessing or post-processing, simplifying the pipeline and potentially improving real-time applicability.

Future research could explore the applicability of this method beyond still image deblurring, for example, in video sequences where temporal coherence provides additional information that could enhance blur estimation. Additionally, extending the motion flow model to accommodate non-linear or more complex motion patterns that arise in dynamic scenes could further refine its applicability in diverse real-world scenarios.

In conclusion, this paper makes a significant contribution to the field of motion deblurring by proposing a paradigm shift in how motion blur is conceptualized and tackled, moving the focus from the blurred image's content to the motion patterns that created the blur. This approach not only challenges existing methods but also sets a precedent for future innovations in image restoration.