Efficient Underwater Image Enhancement with State-Space Modeling: Introducing MambaUIE

Khái niệm cốt lõi

MambaUIE, a novel state-space modeling-based architecture, efficiently enhances underwater images by capturing global contextual information and local fine-grained features.

Tóm tắt

The paper introduces MambaUIE, a state-space modeling-based architecture for efficient underwater image enhancement (UIE). The key highlights are:

MambaUIE is the first UIE model constructed based on the state-space model (SSM) Mamba, which can model long-range dependencies efficiently.

The authors design a Dynamic Interaction-Visual State Space (DI-VSS) block to capture global contextual information at the macro level while mining local fine-grained features at the micro level.

A Spatial Feed-Forward Network (SGFN) is introduced to further enhance Mamba's local modeling capability.

Experiments on the UIEB dataset show that MambaUIE achieves state-of-the-art performance in terms of PSNR and SSIM, while reducing the computational cost by 67.4% compared to previous methods.

The authors demonstrate that MambaUIE can effectively synthesize global and local information for underwater image enhancement, breaking the limitation of FLOPs on accuracy in UIE tasks.

Thống kê

MambaUIE reduces GFLOPs by 67.4% (2.715G) compared to the state-of-the-art method.
On the T90 test set of the UIEB dataset, MambaUIE achieves a PSNR of 25.42 dB and an SSIM of 0.954, outperforming the previous state-of-the-art method NU2Net by 3.001 dB in PSNR and 0.031 in SSIM.

Trích dẫn

"To the best of our knowledge, we are the first to successfully apply Mamba to a UIE task. It provides a new benchmark and reference for exploring more efficient UIE in the future."
"This paper presents a novel architecture, MambaUIE, in which we design the Dynamic Interaction-Visual State Space Block to model global dependencies while capturing local fine-grained features."
"The local modeling capability of Mamba is further enhanced by designing Spatial Feed-Farword Network to improve the model's efficiency."

Thông tin chi tiết chính được chắt lọc từ

MambaUIE&SR: Unraveling the Ocean's Secrets with Only 2.8 FLOPs

by Zhihao Chen,... lúc arxiv.org 04-23-2024

https://arxiv.org/pdf/2404.13884.pdf

Yêu cầu sâu hơn

How can the proposed MambaUIE architecture be extended to other computer vision tasks beyond underwater image enhancement?

The MambaUIE architecture can be extended to other computer vision tasks by leveraging its efficient state-space modeling approach and the integration of global and local information. One way to extend it is by applying it to tasks such as image segmentation, object detection, and image classification. For image segmentation, the MambaUIE's ability to capture long-range dependencies while maintaining linear complexity can be beneficial in accurately delineating object boundaries. By adapting the architecture to incorporate task-specific modules like region proposal networks or semantic segmentation heads, MambaUIE can excel in segmenting objects in complex scenes.
Furthermore, for object detection tasks, the efficient synthesis of global and local information in MambaUIE can aid in accurately localizing and classifying objects in images. By integrating object detection frameworks like Faster R-CNN or YOLO into the architecture, MambaUIE can be tailored to detect objects in various scenarios efficiently.
In image classification, the ability of MambaUIE to capture both global contextual information and local fine-grained features can enhance the model's understanding of image content. By adapting the architecture to include classification heads and leveraging transfer learning techniques, MambaUIE can be applied to classify images into different categories with high accuracy.
Overall, by customizing the MambaUIE architecture for specific computer vision tasks and incorporating task-specific components, it can be extended beyond underwater image enhancement to excel in various computer vision applications.

What are the potential limitations of the state-space modeling approach, and how can they be addressed to further improve the performance of MambaUIE?

While state-space modeling offers advantages such as modeling long-range dependencies and maintaining linear complexity, it also has potential limitations that can impact the performance of models like MambaUIE. One limitation is the challenge of capturing intricate spatial relationships in complex scenes, especially in tasks requiring precise localization or detailed feature extraction. To address this limitation and improve performance, MambaUIE can benefit from incorporating attention mechanisms or spatial transformers to enhance its ability to focus on relevant image regions and features.
Another limitation of state-space modeling is the potential for information loss or distortion during the modeling process, which can affect the quality of the output. To mitigate this, techniques like residual connections or skip connections can be integrated into the MambaUIE architecture to facilitate information flow and gradient propagation, ensuring that crucial details are preserved throughout the model.
Additionally, the interpretability of state-space models can be a challenge, making it difficult to understand the model's decision-making process. To enhance interpretability and transparency, techniques like attention visualization or saliency mapping can be employed to provide insights into the model's reasoning and highlight important image regions influencing the output.
By addressing these potential limitations through the incorporation of attention mechanisms, residual connections, and interpretability techniques, the performance of MambaUIE can be further improved, leading to more accurate and reliable results in various computer vision tasks.

Given the efficiency of MambaUIE, how can it be leveraged to enable real-time underwater image processing on resource-constrained platforms like unmanned underwater vehicles?

The efficiency of MambaUIE makes it well-suited for real-time underwater image processing on resource-constrained platforms like unmanned underwater vehicles (UUVs). To leverage MambaUIE for such applications, several strategies can be implemented:

Model Optimization: Further optimize the MambaUIE architecture by reducing the model size and complexity without compromising performance. Techniques like quantization, pruning, and knowledge distillation can be applied to create a lightweight version of MambaUIE suitable for deployment on UUVs.

Hardware Acceleration: Utilize hardware accelerators like GPUs or specialized chips to speed up the inference process of MambaUIE on UUVs. By leveraging the parallel processing capabilities of these accelerators, real-time image processing can be achieved even with limited computational resources.

On-Device Inference: Implement on-device inference of MambaUIE on UUVs to eliminate the need for continuous data transmission to external servers. By deploying the model directly on the UUVs, latency is reduced, enabling real-time image enhancement during underwater exploration missions.

Dynamic Resource Allocation: Implement dynamic resource allocation strategies to adapt the computational resources allocated to MambaUIE based on the processing requirements of different underwater scenes. By dynamically adjusting resource allocation, the model can maintain real-time performance while optimizing resource utilization.

By implementing these strategies, MambaUIE can be effectively leveraged to enable real-time underwater image processing on resource-constrained platforms like unmanned underwater vehicles, enhancing the efficiency and effectiveness of underwater exploration and research activities.