Depthwise separable process parameter settings.

Infrared and visible image fusion can generate a fusion image with clear texture and prominent goals under extreme conditions. This capability is important for all-day climate detection and other tasks. However, most existing fusion methods for extracting features from infrared and visible images are based on convolutional neural networks (CNNs). These methods often fail to make full use of the salient objects and texture features in the raw image, leading to problems such as insufficient texture details and low contrast in the fused images. To this end, we propose an unsupervised end-to-end Fusion Decomposition Network (FDNet) for infrared and visible image fusion. Firstly, we construct a fusion network that extracts gradient and intensity information from raw images, using multi-scale layers, depthwise separable convolution, and improved convolution block attention module (I-CBAM). Secondly, as the FDNet network is based on the gradient and intensity information of the image for feature extraction, gradient and intensity loss are designed accordingly. Intensity loss adopts the improved Frobenius norm to adjust the weighing values between the fused image and the two raw to select more effective information. The gradient loss introduces an adaptive weight block that determines the optimized objective based on the richness of texture information at the pixel scale, ultimately guiding the fused image to generate more abundant texture information. Finally, we design a single and dual channel convolutional layer decomposition network, which keeps the decomposed image as possible with the input raw image, forcing the fused image to contain richer detail information. Compared with various other representative image fusion methods, our proposed method not only has good subjective vision, but also achieves advanced fusion performance in objective evaluation.

红外与可见光图像融合技术可在极端环境下生成纹理清晰、目标突出的融合图像，该能力对于全天候气候监测等任务具有重要价值。然而，当前多数用于提取红外与可见光图像特征的融合方法均基于卷积神经网络（Convolutional Neural Networks, CNNs），此类方法往往未能充分利用原始图像中的显著目标与纹理特征，进而导致融合图像存在纹理细节不足、对比度偏低等问题。为此，本文提出一种用于红外与可见光图像融合的无监督端到端融合分解网络（Fusion Decomposition Network, FDNet）。首先，本文构建了一款融合网络，该网络借助多尺度层、深度可分离卷积以及改进型卷积块注意力模块（Improved Convolution Block Attention Module, I-CBAM）从原始图像中提取梯度与强度信息。其次，由于FDNet基于图像的梯度与强度信息开展特征提取，本文据此设计了梯度损失与强度损失函数：强度损失采用改进型弗罗贝尼乌斯范数（Frobenius norm）调节融合图像与两幅原始图像间的权重分配，以筛选更有效的信息；梯度损失则引入了自适应权重模块，该模块可基于像素尺度上的纹理信息丰富度确定优化目标，最终引导融合图像生成更为丰富的纹理细节。最后，本文设计了单双通道卷积层分解网络，该网络可使分解后的图像尽可能贴合输入的原始图像，从而迫使融合图像包含更为丰富的细节信息。与多种其他代表性图像融合方法相比，本文所提方法不仅具备优异的主观视觉效果，在客观评估指标上也达到了领先的融合性能。