Infrared and Visible Image Fusion with ResNet and zero-phase component analysis (1806.07119v7)

Abstract: Feature extraction and processing tasks play a key role in Image Fusion, and the fusion performance is directly affected by the different features and processing methods undertaken. By contrast, most of deep learning-based methods use deep features directly without feature extraction or processing. This leads to the fusion performance degradation in some cases. To solve these drawbacks, we propose a deep features and zero-phase component analysis (ZCA) based novel fusion framework is this paper. Firstly, the residual network (ResNet) is used to extract deep features from source images. Then ZCA is utilized to normalize the deep features and obtain initial weight maps. The final weight maps are obtained by employing a soft-max operation in association with the initial weight maps. Finally, the fused image is reconstructed using a weighted-averaging strategy. Compared with the existing fusion methods, experimental results demonstrate that the proposed framework achieves better performance in both objective assessment and visual quality. The code of our fusion algorithm is available at https://github.com/hli1221/imagefusion_resnet50

• The paper presents a novel fusion method that integrates ResNet for deep feature extraction with ZCA for normalization to maximize image clarity.
• Extensive experiments on 21 datasets demonstrate improved metrics like FMI and SSIM, alongside reduced artifacts compared to traditional approaches.
• The framework offers practical benefits for surveillance, medical imaging, and remote sensing, and highlights potential for future scalable enhancements.

Infrared and Visible Image Fusion with ResNet and Zero-Phase Component Analysis: An Analysis

The paper "Infrared and Visible Image Fusion with ResNet and zero-phase component analysis" proposes a novel technique for fusing infrared and visible imaging data. The fusion aims to maximize the information content in resultant images by capitalizing on the diverse features of different image modalities. The core of the proposed method involves leveraging deep learning architectures, particularly the Residual Network (ResNet), coupled with zero-phase component analysis (ZCA).

Infrared and visible image fusion holds significant importance in various domains such as surveillance, medical imaging, and remote sensing. Traditional fusion methodologies often relied on manual feature extraction, which can be cumbersome and less optimal with the growing complexity of image data and sensor capabilities. Recent representation learning and deep learning techniques have demonstrated superior capabilities in extracting and utilizing intricate features for fusion tasks. This paper exploits such advancements by combining ResNet to automatically extract and process features from the image data and refine those features using ZCA to enhance fusion performance.

Methodology

The proposed methodology utilises ResNet, a deep convolutional neural network known for its robustness in deep image analysis tasks, to extract deep features from the source images. The extracted features undergo normalization through ZCA, which serves to decorate and adaptively weight the feature set, thereby mitigating issues related to feature distribution and representation.

The utilization of ZCA in conjunction with ResNet marks a pivotal aspect of the fusion approach by aligning the deep feature distributions into a common, sparse space, making them more amenable to subsequent processing steps. The initial weight maps are derived through local average $l_1$-norm calculations on these features. Subsequently, bicubic interpolation adjusts these weights for integration with the source images.

Reconstruction of the fused image is accomplished through a weighted-averaging strategy anchored on adaptive weight maps, derived through a soft-max operation over initial maps. This layered architecture ensures the preservation of critical spatial and detail information from the original images, balanced across the different modalities.

Numerical and Visual Performance

The paper reports extensive testing over 21 datasets, comparing the proposed method against well-established fusion techniques. The results indicate superior performance in both objective fidelity—assessed using the $FMI_{pixel}$, $FMI_{dct}$, $FMI_w$, $N_{abf}$, $SSIM_a$, and edge preservation indices—and subjective assessment.

The integration of deep feature extraction (ResNet) and mathematical normalization (ZCA) outperforms many of the existing fusion methods by yielding clearer, noise-free fused images. Importantly, $N_{abf}$ values were demonstrably lower, signaling reduced artifacts and distortions in the fused results, with a notable maintenance of structural integrity confirmed via $SSIM_a$ and $EPI_a$ values.

Implications and Future Research

This research contributes to advancing the field of image fusion by integrating dynamic feature extraction techniques with robust mathematical preprocessing, offering a scalable and efficient solution adaptable to various imaging challenges. The robust framework combines the strengths of convolutional neural networks with traditional signal processing insights to enhance multimodal imaging applications.

For future development, exploration into scalable networks, beyond ResNet and ZCA combinations, might further optimize the performance and efficiency of the fusion process. Additionally, applying this framework to more diverse and higher-resolution datasets can test its limits and uncover new potential enhancements in fusion strategies.

The proposed method’s robustness and adaptability herald significant implications across various industries, highlighting the practical viability of harmonizing modern deep learning techniques with statistical analyses to achieve highly informative and reliable fused image outputs.