为了在红外与可见光图像融合中充分利用中间层提取的信息,防止信息过度丢失,提出一种新的基于卷积自编码器和残差块的图像融合方法。该方法采用由编码器、融合层和解码器三部分组成的网络结构。将残差网络引入编码器中,将红外与可见光图像分别送入编码器后,通过卷积层和残差块来获取图像的特征图;将得到的特征图采用改进的基于L1-norm的相似度融合策略进行融合,并将其整合为一个包含源图像显著特征的特征图;重新设计损失函数,利用解码器对融合后的图像进行重构。实验结果表明,与其他融合方法相比,该方法有效地提取并保留了源图像的深层信息,融合结果在主观和客观评价中都有着一定的优势。

In order to make full use of the information extracted from the middle layer and prevent information from losing excessively, a new image fusion method based on a convolutional auto-encoder and a residual block is proposed, which is composed of an encoder, a fusion layer, and a decoder. First, the residual network is introduced into the encoder, the infrared and visible images are fed into the encoder, and the convolution layer and residual block are used to obtain the feature map of the image. Then, the obtained feature map is fused by using an improved fusion strategy based on L1-norm similarity, which is integrated into a feature map containing the salient features of the source image. Finally, the loss function is redesigned and the decoder is used to reconstruct the fused image. The experimental results show that compared with other fusion methods, the method effectively extracts and preserves the deep information of the source image, which makes the fusion result have certain advantages in both subjective and objective evaluation.