Fully convolutional network-based infrared and visible image fusion

Feng, Yufang; Lu, Houqing; Bai, Jingbo; Cao, Lin; Yin, Hong

doi:10.1007/s11042-019-08579-w

Cited by 13 publications

(7 citation statements)

References 32 publications

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“…Vanmali et al [11] addressed the thermal radiation problem in an Infrared-Visible (I-V) IF with a Hybrid Image Filtering technique derived from the Divide-and-Conquer strategy. Feng et al [12] use Fully Convolutional Network (FCN) for fusing I-V pictures by applying "Local Non-Subsampled Shearlet Transform (LNSST)" and Average Gradient (AVG) as fusion rule and got the High-Quality visuals, objective assessments. Laplacian Pyramid (LP) and Max-Absolute as fusion rule to fuse I-V images [13].…”

Section: Related Workmentioning

confidence: 99%

Classification Similarity Network Model for Image Fusion Using Resnet50 and GoogLeNet

Sreedhar¹,

Nandhagopal²

2022

Intelligent Automation &Amp; Soft Computing

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The current trend in Image Fusion (IF) algorithms concentrate on the fusion process alone. However, pay less attention to critical issues such as the similarity between the two input images, features that participate in the Image Fusion. This paper addresses these two issues by deliberately attempting a new Image Fusion framework with Convolutional Neural Network (CNN). CNN has features like pre-training and similarity score, but functionalities are limited. A CNN model with classification prediction and similarity estimation are introduced as Classification Similarity Networks (CSN) to address these issues. ResNet50 and GoogLeNet are modified as the classification branches of CSN v1, CSN v2, respectively, to reduce feature dimensions. IF rules depend on the input dataset to fusion the extracted features. The output of the fusion process is fed into CSN v3 to improve the output image quality. The proposed CSN model is pre-trained and Fully Convolutional. At the time of IF, consider the similarities between the input images. This model applies to Multi-Focus, Multi-Modal Medical, Infrared-Visual and Multi-Exposure image datasets, and analyzed outcomes. The suggested model shows a significant improvement than the modern IF algorithms.

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Section: Related Workmentioning

confidence: 99%

Classification Similarity Network Model for Image Fusion Using Resnet50 and GoogLeNet

Sreedhar¹,

Nandhagopal²

2022

Intelligent Automation &Amp; Soft Computing

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“…The fused image is obtained using that decision map and the input images. A new fully CNN-based IR and VI fusion-based method is proposed in [35] that uses local non-subsampled shearlet transform (LNSST) to decompose the given images into low-frequency and high-frequency sub-bands. The high-frequency coefficients are fed into CNN to obtain the weight map.…”

Section: Introductionmentioning

confidence: 99%

An Improved Infrared and Visible Image Fusion Using an Adaptive Contrast Enhancement Method and Deep Learning Network with Transfer Learning

Bhutto

Tian

et al. 2022

Remote Sensing

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Deep learning (DL) has achieved significant attention in the field of infrared (IR) and visible (VI) image fusion, and several attempts have been made to enhance the quality of the final fused image. It produces better results than conventional methods; however, the captured image cannot acquire useful information due to environments with poor lighting, fog, dense smoke, haze, and the noise generated by sensors. This paper proposes an adaptive fuzzy-based preprocessing enhancement method that automatically enhances the contrast of images with adaptive parameter calculation. The enhanced images are then decomposed into base and detail layers by anisotropic diffusion-based edge-preserving filters that remove noise while smoothing the edges. The detailed parts are fed into four convolutional layers of the VGG-19 network through transfer learning to extract features maps. These features maps are fused by multiple fusion strategies to obtain the final fused detailed layer. The base parts are fused by the PCA method to preserve the energy information. Experimental results reveal that our proposed method achieves state-of-the-art performance compared with existing fusion methods in a subjective evaluation through the visual experience of experts and statistical tests. Moreover, the objective assessment parameters are conducted by various parameters (FMI, SSIMa, API, EN, QFAB, and NFAB) which were used in the comparison method. The proposed method achieves 0.2651 to 0.3951, 0.5827 to 0.8469, 56.3710 to 71.9081, 4.0117 to 7.9907, and 0.6538 to 0.8727 gain for FMI, SSIMa, API, EN, and QFAB, respectively. At the same time, the proposed method has more noise reduction (0.3049 to 0.0021) that further justifies the efficacy of the proposed method than conventional methods.

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“…In the fusion of low-frequency components, it integrates the fusion rules of local measure of sharpness change (LSCM), local signal strength (LSS), and phase congruency (PC) to achieve the energy preservation and detail extraction of low-frequency components. Feng et al [16] proposed a novel fusion framework for infrared and visual images based on a full convolutional network (FCN) in the local nonsampled shearlet transform (LNSST) domain. Kong et al [17] proposed a novel infrared and visible image fusion method called the structure transferring fusion (STF) method.…”

Section: Introductionmentioning

confidence: 99%

Research on Infrared and Visible Image Fusion Based on Tetrolet Transform and Convolution Sparse Representation

et al. 2021

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Image fusion is a visual enhancement technique that combines source images from different sensors to produce a more robust and informative fused image for subsequent processing or decision making. Infrared and visible light images share complementary properties that enable the production of robust and informative fused images. This paper proposed an infrared and visible image fusion method that improved the tetrolet framework to improve infrared and visible image fusion quality. First, the source image is enhanced by bicubic interpolation. The improved tetrolet transform then decomposes the enhanced source image; the high-frequency components are fused by convolutional sparse representation theory and combined with corresponding rules, and the low-frequency components are fused by defining ISER descriptors. Finally, we use the inverse transform to reconstruct the fused image. Qualitative and quantitative experimental results on five groups of typical infrared and visible image datasets demonstrate the proposed method's effectiveness. The proposed method exhibits better performances on subjective vision and objective indexes compared with the other state-of-the-art methods.INDEX TERMS Image fusion, improved tetrolet transform, convolutional sparse representation, ISER descriptor.

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Fully convolutional network-based infrared and visible image fusion

Cited by 13 publications

References 32 publications

Classification Similarity Network Model for Image Fusion Using Resnet50 and GoogLeNet

Classification Similarity Network Model for Image Fusion Using Resnet50 and GoogLeNet

An Improved Infrared and Visible Image Fusion Using an Adaptive Contrast Enhancement Method and Deep Learning Network with Transfer Learning

Research on Infrared and Visible Image Fusion Based on Tetrolet Transform and Convolution Sparse Representation

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