Spatially-Attentive Patch-Hierarchical Network for Adaptive Motion Deblurring (2004.05343v1)

Abstract: This paper tackles the problem of motion deblurring of dynamic scenes. Although end-to-end fully convolutional designs have recently advanced the state-of-the-art in non-uniform motion deblurring, their performance-complexity trade-off is still sub-optimal. Existing approaches achieve a large receptive field by increasing the number of generic convolution layers and kernel-size, but this comes at the expense of of the increase in model size and inference speed. In this work, we propose an efficient pixel adaptive and feature attentive design for handling large blur variations across different spatial locations and process each test image adaptively. We also propose an effective content-aware global-local filtering module that significantly improves performance by considering not only global dependencies but also by dynamically exploiting neighbouring pixel information. We use a patch-hierarchical attentive architecture composed of the above module that implicitly discovers the spatial variations in the blur present in the input image and in turn, performs local and global modulation of intermediate features. Extensive qualitative and quantitative comparisons with prior art on deblurring benchmarks demonstrate that our design offers significant improvements over the state-of-the-art in accuracy as well as speed.

Spatially-Attentive Patch-Hierarchical Network for Adaptive Motion Deblurring

The paper under consideration presents a novel approach to address the complex challenge of motion deblurring, particularly for dynamic scenes where blurs are non-uniform due to motion variations. The authors introduce the Spatially-Attentive Patch-Hierarchical Network (SAP-HN), which endeavors to enhance the balance between performance and computational efficiency, a prevalent issue in current state-of-the-art models for non-uniform motion deblurring.

Core Contributions

The authors put forward several key contributions:

1. Pixel Adaptive and Feature Attentive Design: The proposed architecture adapts to spatial variations in motion blur, dynamically processing each test image. This adaptive approach acknowledges the non-uniform nature of blur and optimally adjusts the processing for various spatial locations.
2. Content-Aware Global-Local Filtering Module: This novel module considers both global dependencies and neighboring pixel information, integrating these aspects to improve performance beyond traditional CNN-based approaches. This module is integrated into a patch-hierarchical attentive architecture, which provides local and global modulation of features by implicitly acknowledging spatial variations in blur.
3. Efficient Model Design: By utilizing a hierarchical and patch-based processing structure combined with dynamic modules, the authors achieve a significant improvement in deblurring accuracy and inference time, outperforming existing methods on renowned benchmarks.

Methodological Advancements

The SAP-HN leverages recent advances in attention mechanisms, applying them in a manner that is computationally viable for image deblurring tasks. The network incorporates a combination of self-attention and cross-attention mechanisms, providing a multi-faceted approach to identifying and amending blur within images. Furthermore, a pixel-dependent filtering module adjusts both the local filter weights and spatial offsets, attesting to the method's adaptability and robustness.

In an architectural context, the network avoids traditional cascading convolutional layers, opting instead for a patch-hierarchical structure. This strategy, inspired by multiscale processing but devoid of its drawbacks, affords efficient handling of large dynamic blurs while reducing computational overhead.

Performance Evaluation

The effectiveness of SAP-HN is quantitatively and qualitatively validated against state-of-the-art methods on datasets such as GoPro and HIDE. The results indicate an edge of up to 3x improvement in inference speed and meaningful gains in PSNR and SSIM metrics. These empirical findings are aligned with the theoretical underpinnings of the architecture, showcasing its potential for practical implementation in deblurring tasks where real-time processing is crucial.

Theoretical and Practical Implications

The introduction of spatially attentive processing modules within a patch-hierarchical network signifies a noteworthy step towards resolving the traditional trade-off between network depth and receptive field size in CNNs. The proposed structure, which enhances spatial awareness and feature modulation, could be extrapolated to other image restoration tasks that face similar spatial dependency challenges.

This work also opens pathways for future exploration in image processing within limited resource environments. By advancing content-adaptive processing techniques, the paper lays a foundation for developing models that efficiently manage computational resources without sacrificing accuracy, a crucial consideration in deploying solutions in real-world applications such as surveillance and mobile photography.

In summary, the paper delivers a significant evolution in the motion deblurring landscape through the innovative integration of adaptive and spatially-aware processing methodologies, proving its utility in both theoretical exploration and practical deployment within the field of image processing.