DenseFuse: A Fusion Approach to Infrared and Visible Images

Li, Hui; Wu, Xiaojun

doi:10.1109/tip.2018.2887342

Cited by 1,209 publications

(696 citation statements)

References 30 publications

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“…As we can see from this figure, the combined images generated by our method are more visually pleasant than the other approaches, with every part in focus, and with clear and sharp boundary edges. In contrast, the comparison methods, CSR [3], Deepfuse network [44] and Densefuse network [45], all lead to different levels of blurring artefacts across the boundary areas, as shown in the close-ups of the toy dog and the fence.…”

Section: Multi-focus Image Fusionmentioning

confidence: 98%

“…Prabhakar et al [44] proposed a CNN based unsupervised image fusion method to fuse one under-exposed image with an over-exposed one. Li et al [45] proposed a CNN network with the dense block structure to solve the infrared and visible image fusion problem. To improve the perceptual quality of the fused image, Ma [46] proposed a generative adversarial network (GAN), called FusionGAN, for infrared and visual image fusion.…”

Section: Multi-modal Image Fusionmentioning

confidence: 99%

“…The testing images are from the Lytro multi-focus image dataset [72]. We compare our method with the following three state-of-the-art approaches: CSR [3], Deepfuse network [44] and Densefuse network [45]. The visual comparison results are shown in Fig.…”

Section: Multi-focus Image Fusionmentioning

confidence: 99%

“…Fig. 9 shows the fused images using different methods, including CSR [3], Deepfuse network [44], Densefuse network [45] and our CU-Net. As we can see, the method CSR [3] and Deepfuse network [44] both lose the image energy and have low contrast, which leads to low brightness in the soft-tissue regions.…”

Section: Medical Image Fusionmentioning

confidence: 99%

“…As we can see, the method CSR [3] and Deepfuse network [44] both lose the image energy and have low contrast, which leads to low brightness in the soft-tissue regions. The method Densefuse [45] produces over-bright images, which makes many details difficult to see. Our method is able to preserve well the structures and details of the organs and tissues without losing the image energy or producing the brightness distortions.…”

Section: Medical Image Fusionmentioning

confidence: 99%

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Deep Convolutional Neural Network for Multi-Modal Image Restoration and Fusion

Deng

Dragotti

2021

IEEE Trans. Pattern Anal. Mach. Intell.

162

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In this paper, we propose a novel deep convolutional neural network to solve the general multi-modal image restoration (MIR) and multi-modal image fusion (MIF) problems. Different from other methods based on deep learning, our network architecture is designed by drawing inspirations from a new proposed multi-modal convolutional sparse coding (MCSC) model. The key feature of the proposed network is that it can automatically split the common information shared among different modalities, from the unique information that belongs to each single modality, and is therefore denoted with CU-Net, i.e., Common and Unique information splitting network. Specifically, the CU-Net is composed of three modules, i.e., the unique feature extraction module (UFEM), common feature preservation module (CFPM), and image reconstruction module (IRM). The architecture of each module is derived from the corresponding part in the MCSC model, which consists of several learned convolutional sparse coding (LCSC) blocks. Extensive numerical results verify the effectiveness of our method on a variety of MIR and MIF tasks, including RGB guided depth image super-resolution, flash guided non-flash image denoising, multi-focus and multi-exposure image fusion. Index Terms-Multi-modal image restoration, image fusion, multi-modal convolutional sparse coding ! • The authors are with the

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Section: Multi-focus Image Fusionmentioning

confidence: 98%

Section: Multi-modal Image Fusionmentioning

confidence: 99%

Section: Multi-focus Image Fusionmentioning

confidence: 99%

Section: Medical Image Fusionmentioning

confidence: 99%

Section: Medical Image Fusionmentioning

confidence: 99%

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Deep Convolutional Neural Network for Multi-Modal Image Restoration and Fusion

Deng

Dragotti

2021

IEEE Trans. Pattern Anal. Mach. Intell.

162

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EMA‐GAN: A Generative Adversarial Network for Infrared and Visible Image Fusion with Multiscale Attention Network and Expectation Maximization Algorithm

Xi,

Jin,

Jiang

et al. 2023

Advanced Intelligent Systems

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The purpose of the infrared and visible image fusion is to generate a fused image with rich information. Although most fusion methods can achieve good performance, there are still shortcomings in extracting feature information from source images, which make it difficult to balance the thermal radiation region information and texture detail information in the fused image. To address the above issues, an expectation maximization (EM) learning framework based on adversarial generative networks (GAN) for infrared and visible image fusion is proposed. The EM algorithm (EMA) can obtain maximum likelihood estimation for problems with potential variables, which is helpful in solving the problem of lack of labels in infrared and visible image fusion. The axial‐corner attention mechanism is designed to capture long‐range semantic information and texture information of the visible image. The multifrequency attention mechanism digs the relationships between features at different scales to highlight target information of infrared images in the fused result. Meanwhile, two discriminators are used to balance two different features, and a new loss function is designed to maximize the likelihood estimate of the data with soft class label assignments, which is obtained from the expectation network. Extensive experiments demonstrate the superiority of EMA‐GAN over the state‐of‐the‐art.

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Multiscale channel attention network for infrared and visible image fusion

Zhu

Dou

Jian

et al. 2020

Concurrency and Computation

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Imaging systems with different imaging sensors are widely applied to surveillance field, military field, and medicine field. Particularly, infrared imaging sensors can acquire thermal radiations emitted by different objects but lack textural details, and visible imaging sensors can capture abundant textural information but suffer from loss of scene information under poor weather conditions. The fusion of infrared and visible images can synthesize a new image with complementary information of the source images. In this paper, we present a deep learning method with encoder–decoder architecture for infrared and visible image fusion. Firstly, multiscale channel attention blocks are introduced to extract features at different scales, which can preserve more meaningful information and enhance the important information. Secondly, we utilize the improved fusion strategy based on visual saliency to fuse feature maps. Lastly, the fusion result is restored via reconstruction network. In comparison with other state‐of‐the‐art approaches, our experimental results achieve appealing performance on visual effects and objective assessments.

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DenseFuse: A Fusion Approach to Infrared and Visible Images

Cited by 1,209 publications

References 30 publications

Deep Convolutional Neural Network for Multi-Modal Image Restoration and Fusion

Deep Convolutional Neural Network for Multi-Modal Image Restoration and Fusion

EMA‐GAN: A Generative Adversarial Network for Infrared and Visible Image Fusion with Multiscale Attention Network and Expectation Maximization Algorithm

Multiscale channel attention network for infrared and visible image fusion

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