<p>In many scenarios, visibility in visible (Vis) images is significantly compromised due to atmospheric conditions such as haze, fog, and mist. Near-infrared (NIR) sensors, however, can capture images in these conditions without being affected by the obscurants. Despite this advantage, NIR images often suffer from poor texture and color representation of scene elements and are particularly susceptible to mixed Poisson-Gaussian noise, unlike Vis images. This article presents an efficient image enhancement technique that seamlessly integrates image fusion with denoising to effectively address the associated challenges. This method comprises four primary steps: image decomposition using a combination of fast bilateral filter (FBF) and weighted guided filter (WGF), fusion of the low frequency (LF) layers with enhanced visibility, and the integration of high frequency (HF) layers using a modified dual domain filter (MDDF) based fusion strategy. The image decomposition process itself involves three key stages: extraction of LF and HF components, approximation of the HF components, and detailed layer extraction. The effectiveness of the proposed technique is validated through extensive experimental evaluations, wherein its performance is compared against several state-of-the-art visible and near-infrared (NIR) image fusion methods. An ablation study is also conducted using various filtering strategies to assess the individual contributions of each component. The results demonstrate the superior performance of the proposed approach, particularly in scenarios where NIR images are degraded by mixed Poisson-Gaussian noise, yielding fused images with significantly enhanced visibility and reduced noise levels.</p>