# Infrared and Visible Image Fusion via Feature-Oriented Dual-Module Complementary
###### Abstract  
With the industrial demand caused by multi-sensor image fusion, infrared and visible image fusion (IVIF) technology is flourishing. In recent years, scale decomposition methods have led the trend for feature extraction. Such methods, however, have low time efficiency. To address this issue, this paper proposes a simple yet effective IVIF approach via a feature-oriented dual-module complementary. Specifically, we analyze five classical operators comprehensively and construct the spatial gradient capture module (SGCM) and infrared brightness supplement module (IBSM). In the SGCM, three kinds of feature maps are obtained, respectively, by introducing principal component analysis, saliency, and proposing contrast estimation operators considered the relative differences of contrast information covered by the input images. These maps are later reconstructed through pyramidal transformation to obtain the predicted image. The IBSM is then proposed to refine the missing infrared thermal information in the predicted image. Among them, we improve the measurement operators applied to the exposure modalities, namely, the gradient of the grayscale images (2D gradient) and well-exposedness. The former is responsible for extracting fine details, and the latter is meant for locating brightness regions. Experiments performed on public datasets demonstrate that the proposed method outperforms nine state-of-the-art methods in terms of subjective visual and objective indicators. infrared and visible image fusion; feature-oriented dual-module complementary; spatial gradient capture module; infrared brightness supplement module +
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first locates the infrared target region and then combines the sub-images decomposed by the NSCT to obtain the final fused image with good retention of targets and details. [PERSON] et al. [14] proposed an IVIF method that uses a guided filter to decompose input images to obtain two sub-layers; this method also combines the cumulative distributions of the gray levels and entropy to adaptively preserve the infrared targets and visible textures. Although these methods produce better subjective effects and higher fusion efficiencies, they use only a single filter to decompose the source image, which results in loss of image features to a certain extent. [PERSON] et al. [15] proposed a multi-scale decomposition method based on a rolling guided filter and a fast bilateral filter to decompose the input images into sublayers. Following this, sparse representations and a detail injection model are used to obtain the fused result with abundant information. [PERSON] et al. [16] proposed a guided filter and multi-directional filter banks, where the filter is used to separate the source image into its base and detail layers while the filter bank is applied to fuse the base layers; this combination was shown to achieve better fusion performance. [PERSON] et al. [17] proposed an IVIF scheme based on visibility enhancement and hybrid multiscale decomposition to obtain the base and detail layers, and the weights of the visual saliency illumination map and a convolutional neural network (CNN) were used to process the corresponding sublayers. Compared to a single filter, the hybrid filter obtains finer details and brighter infrared targets, but the fused image is obtained at the expense of loss of algorithmic efficiency. The second category includes deep learning (DL)-based methods, which have substantially improved and achieved research results [3; 4; 9; 18; 19; 20]. [PERSON] et al. [18] first applied the deep CNN to IVIF to extract features and calculate the activity level to generate feature maps, thereby obtaining fused images. [PERSON] et al. [19] constructed an \"encoder-fusion-strategy-decoder\" framework to achieve high-quality results. [PERSON] et al.