# Infrared and Visible Image Fusion via Feature-Oriented Dual-Module Complementary
###### Abstract
[20] employed a generative adversarial network (GAN) for guiding IVIF for the first time, in which the GAN was similar to an adversarial game between an input image and an imaginary image generated from the input image by setting the loss function to continuously adjust the weights to obtain a near-perfect fused image. To summarize, scale-transform-based methods can obtain sub-layers at different scales with the help of a decomposition algorithm. Thereafter, suitable fusion rules related to image context information are designed to guide the fusion of these sub-layers. However, these methods are inherently flawed as they involve manual comparison, determining the optimal number of decomposition layers, and selecting the fusion rules; the decomposition processing is time-consuming, leading to poor real-time performance. Although DL-based methods have powerful feature extraction functions, they need sufficient raw data and strong computing resources to train models. These methods also lack convincing theoretical knowledge to explain or evaluate the pros and cons of the networks. Motivated by the abovementioned discussion, we propose an feature-oriented dual-module complementary IVIF. Specifically, we analyzed five classical operators to replace the potential pitfalls of using scale decomposition filters to extract features according to the original image characteristics, and we comprehensively propose two feature extraction modules, namely, the spatial gradient capture module (SGCM) and infrared brightness supplement module (IBSM). As the name suggests, the former is more focused on preserving the spatial gradient information from the original images, which is constructed using principal component analysis (PCA), saliency, and contrast estimation operators. The latter compensates for the feature loss by focusing on improving two exposure metrics that are closely related to image intensity. Extensive experiments were performed on public datasets to prove that the proposed method has better fusion performance in terms of the overall contrast and feature preservation compared to other existing state-of-the-art fusion methods. The main contributions of this paper can be summarized as follows:  
* We propose IVIF via a feature-oriented dual-module complementary. Based on the varying input image characteristics, we analyzed five classical operators to replace the potential limitations of using scale decomposition filters to extract the features and constructed the two modules, SGCM and IBSM. Owing to the complementarity of these two modules, the fused image shows good performance with adequate contrast and high efficiency. * We design a contrast estimator to adaptively transfer useful details from the original image, which helps to obtain predicted images with good information saturation. Based on the predicted image, a complementary module is proposed to preserve the color of the visible image while injecting infrared information to generate a realistic fused image. * We introduce and improve the exposure metrics, namely, the gradient of grayscale (2D gradient) that is responsible for extracting the fine details and well-exposedness for locating the brightness regions. Using these, the infrared information is extracted from the source image and injected into the fused image to highlight the infrared target. The remainder of this paper is organized as follows. Section 2 briefly introduces the related works. Section 3 describes the proposed IVIF method in detail. Section 4 provides the experimental settings and results analysis, followed by the conclusions in Section 5.