NestFuse: An Infrared and Visible Image Fusion Architecture based on Nest Connection and Spatial/Channel Attention Models (2007.00328v2)

Abstract: In this paper we propose a novel method for infrared and visible image fusion where we develop nest connection-based network and spatial/channel attention models. The nest connection-based network can preserve significant amounts of information from input data in a multi-scale perspective. The approach comprises three key elements: encoder, fusion strategy and decoder respectively. In our proposed fusion strategy, spatial attention models and channel attention models are developed that describe the importance of each spatial position and of each channel with deep features. Firstly, the source images are fed into the encoder to extract multi-scale deep features. The novel fusion strategy is then developed to fuse these features for each scale. Finally, the fused image is reconstructed by the nest connection-based decoder. Experiments are performed on publicly available datasets. These exhibit that our proposed approach has better fusion performance than other state-of-the-art methods. This claim is justified through both subjective and objective evaluation. The code of our fusion method is available at https://github.com/hli1221/imagefusion-nestfuse

• The paper introduces a novel fusion architecture that integrates multi-scale CNN feature extraction with spatial and channel attention models.
• It employs a nest connection framework in the encoder-decoder design to bridge semantic gaps and preserve both fine and coarse image details.
• Extensive evaluations show improved metrics such as entropy, mutual information, and tracking robustness compared to state-of-the-art fusion methods.

Overview of NestFuse: An Infrared and Visible Image Fusion Architecture

The paper "NestFuse: An Infrared and Visible Image Fusion Architecture based on Nest Connection and Spatial/Channel Attention Models" proposes a sophisticated method for image fusion, particularly aimed at the integration of infrared and visible spectral images. The central innovation of NestFuse is its utilization of a nest connection-based network architecture supplemented by spatial and channel attention models. This approach seeks to address the inherent challenges in multi-modal image fusion by enhancing the preservation and integration of informative features from input images at various scales.

Components of the NestFuse Architecture

The NestFuse model is architecturally composed of three primary components: the encoder, the fusion strategy, and the decoder. The encoder is responsible for extracting multi-scale deep features from the source images. Leveraging convolutional neural network (CNN) approaches, the encoder decomposes the input images into progressively abstract features across multiple scales. This decomposition is instrumental in capturing both coarse and fine details from the images.

The core novelty lies in the fusion strategy, which forms the backbone of NestFuse. This strategy employs both spatial and channel attention models. The spatial attention model assesses the importance of each spatial position in the feature maps, while the channel attention model evaluates the significance of each channel. Together, these models dynamically prioritize features that are crucial for preserving complementary and salient information during the fusion process. The resultant fused features are then reconstructed into a coherent image via the decoder, which uses the nest connection structure to mitigate semantic gaps and enhance feature utilization across layers.

Experimental Results and Evaluation

The efficacy of NestFuse is quantitatively substantiated through rigorous experiments conducted on publicly available datasets, featuring a variety of infrared and visible image pairs. The results, evaluated on metrics such as entropy, standard deviation, and mutual information, illustrate superior performance of NestFuse in capturing detailed and meaningful information compared to state-of-the-art methods. The paper presents comprehensive evaluations using metrics like $FMI_{dct}$ and $SSIM_a$, showcasing improvements over existing approaches.

Practical implications of NestFuse are also significant, as demonstrated by its application to visual object tracking tasks. By fusing multi-modal data, NestFuse aids in enhancing tracker robustness across challenging scenarios, thereby suggesting potential utility across diverse real-world applications such as surveillance and autonomous navigation systems.

Theoretical Implications and Future Directions

The introduction of spatial and channel attention models within a multi-scale framework exemplifies a significant step forward in the fusion of multi-modal data in image processing. This research contributes to the theoretical understanding of how nested architectures and attention mechanisms can synergistically enhance feature integration. Particularly, the concept of employing a nest connection architecture addresses the prevalent issues of semantic gaps in fusion networks, leading to smoother and more coherent image outputs.

Looking ahead, the NestFuse framework opens several avenues for future research. One potential development is the exploration of its applicability to other domains of multi-modal fusion, such as medical image fusion or satellite image analysis. Additionally, the scalability of NestFuse's components could be investigated, facilitating enhancements to processing efficiency or adaptation to other deep learning-based vision models.

Conclusion

In conclusion, the NestFuse architecture represents a methodically crafted and effectively executed approach to infrared and visible image fusion. By integrating a nest-connection framework with sophisticated attention-based fusion strategies, it achieves notable advancements both in preserving the intricate details of source images and in generating superior fused outputs. Its promising results advocate for further exploration and development, projecting NestFuse as a potentially pivotal model in advancing the field of image fusion.