A Novel State Space Model with Local Enhancement and State Sharing for Efficient Image Fusion

Belangrijkste concepten

The proposed LE-Mamba network utilizes a local-enhanced vision Mamba (LEVM) block and a state sharing technique to effectively capture both local and global spatial information as well as spatial-spectral interactions, leading to state-of-the-art performance in image fusion tasks.

Samenvatting

The paper presents a novel approach called LE-Mamba for efficient image fusion, particularly in the tasks of multispectral pansharpening and multispectral-hyperspectral image fusion.

Key highlights:

1. The authors propose a local-enhanced vision Mamba (LEVM) block that can effectively capture both local and global spatial information. The LEVM block consists of a local VMamba block and a global VMamba block.
2. A state sharing technique is introduced to enable interaction between spatial and spectral information within the state space model (SSM). This includes an adjacent flow and a skip-connected flow to propagate state information across layers.
3. The overall LE-Mamba network is built upon a multi-scale U-Net-like architecture, with the LEVM blocks and state sharing technique incorporated.
4. Extensive experiments on multispectral pansharpening and multispectral-hyperspectral fusion datasets demonstrate the state-of-the-art performance of the proposed LE-Mamba approach.
5. Ablation studies validate the effectiveness of the LEVM block and state sharing technique in boosting the fusion performance.

Statistieken

The proposed LE-Mamba achieves 85% memory reduction and 65% FLOPs reduction compared to self-attention and Swin Transformer, respectively.

Citaten

"The proposed LE-Mamba can achieve superior fusion performance."
"The state sharing technique can reduce information loss and enable simultaneous learning of spatial and spectral information within the state space model (SSM)."

Belangrijkste Inzichten Gedestilleerd Uit

A Novel State Space Model with Local Enhancement and State Sharing for Image Fusion

by Zihan Cao,Xi... om arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.09293.pdf

Diepere vragen

How can the proposed LE-Mamba be extended to other vision tasks beyond image fusion

The proposed LE-Mamba architecture can be extended to other vision tasks beyond image fusion by leveraging its multi-scale structure and the local-enhanced vision Mamba (LEVM) block. For tasks like image classification, object detection, and semantic segmentation, the LEVM block can enhance the network's ability to capture local and global spatial information, improving feature representation and extraction. Additionally, the state sharing technique can be beneficial in tasks requiring interaction between spatial and spectral information, such as image restoration or super-resolution. By adapting the LE-Mamba architecture to different vision tasks, researchers can explore its effectiveness in various domains and potentially achieve state-of-the-art results.

What are the potential limitations of the state sharing technique, and how can it be further improved

One potential limitation of the state sharing technique in the LE-Mamba architecture is the reliance on a fixed parameter 𝛼 to balance the information between the S2L and input features. This fixed parameter may not be optimal for all datasets or tasks, leading to suboptimal performance. To address this limitation, the state sharing technique can be further improved by introducing adaptive mechanisms to dynamically adjust 𝛼 based on the characteristics of the input data. Techniques like learnable parameters or attention mechanisms can be incorporated to allow the network to learn the optimal balance between spatial and spectral information sharing. Additionally, exploring different architectures or variations of the state sharing technique can help enhance its flexibility and adaptability to diverse datasets and tasks.

How can the LE-Mamba architecture be adapted to handle very high-resolution images while maintaining its efficiency

To adapt the LE-Mamba architecture to handle very high-resolution images while maintaining efficiency, several strategies can be implemented. One approach is to incorporate hierarchical processing, where the input image is progressively downsampled and processed at different scales within the network. This hierarchical processing can help manage the computational complexity of handling high-resolution images while preserving important details. Additionally, techniques like parallel processing or efficient convolutional operations can be utilized to optimize the network's performance on large images. By carefully designing the network architecture and incorporating efficient processing strategies, the LE-Mamba architecture can effectively handle very high-resolution images without compromising efficiency.