Abstract: To address challenges such as blurry edges, low contrast, and unclear details in infrared images used in artillery shooting, night vehicle reconnaissance, aerospace, and soldiers' patrolling, this study proposes an enhanced Retinex image enhancement algorithm. The method integrates wavelet transform, improved bilateral filtering, an enhanced threshold function denoising algorithm, and fuzzy set functions. First, the infrared image undergoes wavelet decomposition to extract low and high-frequency coefficients. Subsequently, high-frequency components are enhanced using an improved threshold function, adapting σr for denoising purposes. An improved bilateral filtering Retinex algorithm is employed to smooth the infrared image while preserving essential details. The high and low-frequency components are recombined through wavelet reconstruction to reconstruct the enhanced infrared image. A fuzzy set function is applied to further enhance the contrast of the infrared image. Experimental results validate the effectiveness of the proposed algorithm. It effectively reduces noise, enriches image details, suppresses background interference, and enhances contrast compared to conventional methods such as adaptive histogram equalization and multi-scale Retinex image enhancement. This approach not only enhances the quality of infrared images for critical applications but also demonstrates significant improvements over existing methods in terms of clarity and detail retention.