This paper proposes an effective infrared and visible image fusion method based on a texture conditional generative adversarial network (TC-GAN). The constructed TC-GAN generates a combined texture map for capturing gradient changes in image fusion. The generator in the TC-GAN is designed as a codec structure for extracting more details, and a squeeze-and-excitation module is applied to this codec structure to increase the weight of significant texture information in the combined texture map. The generator loss function is designed by combing the gradient loss and adversarial loss to retain the texture information of the source images. The discriminator brings the texture of the generated image closer to the visible image. To obtain significant texture information from the source images, a multiple decision map-based fusion strategy is proposed using a combined texture map and an adaptive guided filter. Extensive experiments on the public TNO and RoadScene datasets demonstrate that the proposed method is superior to other state-of-the-art algorithms in terms of a subjective evaluation and quantitative indicators.

Multi-focus image fusion is an important technique that aims to generate a single clean image by fusing multiple input images. In this paper, we propose a novel multi-level features convolutional neural network (MLFCNN) architecture for image fusion. In the MLFCNN model, all features learned from previous layers are passed to the subsequent layer. Inside every path between the previous layer and the subsequent layer, we add a 1x1 convolution module to reduce the redundancy. In our method, the source images first are fed to our pre-trained MLFCNN model to obtain the initial focus map. Then, the initial focus map is performed by morphological opening and closing operations and followed by a Gaussian filter to obtain the final decision map. Finally, the fused all-in-focus image is generated based on a weighted-sum strategy with the decision map. The experimental results demonstrate that the proposed method outperforms some state-of-the-art image fusion algorithms in terms of both qualitative and objective evaluations.

The goal of infrared and visible image fusion is to generate an integrated image which can simultaneously preserve more visible detail information and prominent information from the input images. In order to achieve this goal, a novel infrared and visible fusion method based on visual saliency sparse representation and detail injection model is presented. The proposed fusion method contains four steps. The first step is to decompose the source images into base layers and detail layers through a proposed multi-scale decomposition method, which has the advantages of scale awareness and high edge-preservation efficiency. Then, we design a novel sub-fusion rule called visual saliency sparse representation to get the fused base layer. Third, a detail injection model is developed to fuse the detail layers. In this model, we first employ a “max-absolute” scheme to obtain pre-fused images, which are then used as the model items for participating in the fusion process of the detail layers. Finally, the target merged image is achieved through an inverse multi-scale decomposition. Experimental results demonstrate that the proposed method can not only preserve the details and significant information of source images but also enhance the brightness of the fused image, when compared to other state-of-the-art fusion methods.

Multimodal medical image fusion, emerging as a hot topic, aims to fuse images with complementary multi-source information. In this paper, we propose a novel multimodal medical image fusion method based on structural patch decomposition (SPD) and fuzzy logic technology. First, the SPD method is employed to extract two salient features for fusion discrimination. Next, two novel fusion decision maps called an incomplete fusion map and supplemental fusion map are constructed from salient features. In this step, the supplemental map is constructed by our defined two different fuzzy logic systems. The supplemental and incomplete maps are then combined to construct an initial fusion map. The final fusion map is obtained by processing the initial fusion map with a Gaussian filter. Finally, a weighted average approach is adopted to create the final fused image. Additionally, an effective color medical image fusion scheme that can effectively prevent color distortion and obtain superior diagnostic effects is also proposed to enhance fused images. Experimental results clearly demonstrate that the proposed method outperforms state-of-the-art methods in terms of subjective visual and quantitative evaluations.

The purpose of remote sensing image fusion is to sharpen a low spatial resolution multispectral (MS) image by injecting the detail map extracted from a panchromatic (PAN) image. In this paper, a novel remote sensing image fusion method based on adaptive intensity-hue-saturation (IHS) and multiscale guided filter is presented. In the proposed method, the intensity component is obtained adaptively from the upsampled MS image at first. Different from traditional IHS-based methods, we subsequently propose a multiscale guided filter strategy to filter the PAN image to achieve more detail information. Finally, the total detail map is injected into each band of the upsampled MS image to obtain the fused image by a model-based algorithm, in which an improved injection gains approach is proposed to control the quantity of the injected detail information. Experimental results demonstrated that the proposed method can provide more spatial information and preserve more spectral information compared with several state-of-the-art fusion methods in both subjective and objective evaluations.