Infrared (IR) and visible image fusion is a crucial technique in data fusion and image processing. It allows for the accurate integration of thermal radiation and texture details from source images. However, current methods frequently overlook the challenge of high-contrast fusion, resulting in suboptimal performance when replacing thermal radiation target information in IR images with high-contrast information from visible images. To overcome this limitation, a contrast- balanced framework for IR and visible image fusion has been developed. The innovative approach includes a contrast balance strategy for processing visible images, reducing energy while compensating for overexposed areas in detail. Additionally, a contrast-preserving guided filter decomposes the image into energy-detail layers to filter high contrast and information effectively. To extract active information from the detail layer and brightness information from the energy layer, an image statistics technique and a Gaussian distribution of image entropy schemes are introduced for fusing the detail and energy layers. The final fused result is achieved by combining these layers. The final fused result is achieved by combining the detail and energy layers. Comprehensive experiments demonstrate that the proposed method effectively reduces contrast issues while preserving fine details. Additionally, the proposed approach outperformed leading techniques in both qualitative and quantitative evaluations.