Abstract: To solve problems associated with infrared and visible light image fusion algorithms, such as the fact that the infrared target is not prominent and the overall contrast of the fused image is low, this study investigated an infrared and visible light image fusion algorithm based on target extraction and rolling-guided filtering (RGF). First, the RGF-based contrast enhancement algorithm was used to preprocess the visible light image, and the iterative guided infrared patch tensor model (IGPT) was used to extract the salient targets from the infrared image. Then, the RGF was used to perform multiscale decomposition of the infrared and enhanced visible light images to obtain their respective base and detail layers. The extracted infrared target was used, along with the entropy value comparison method, to guide the base layer fusion; at the same time, the regional energy and modified Laplacian energy were used to construct a weight matrix, and the absolute value method was used to guide the detail layer fusion. The final fusion result was obtained through image reconstruction. Experimental results show that the algorithm developed in this study could not only effectively preserve prominent infrared target features and rich texture detail information, but also demonstrate clear comprehensive advantages over existing methods such as ADF, FDPE, LatLRR, VSM-WLS, SeAFusion, and U2Fusion in terms of four quality evaluation metrics: information entropy, standard deviation, mutual information, and visual perception measurement.