<p>Airborne particles, such as smoke and dust, can significantly degrade the clarity of images and videos by producing haze through light attenuation and scattering. Hazy media often exhibit reduced contrast and the loss of critical information, presenting challenges in various applications. To address this issue, we propose a comprehensive dehazing technique that integrates noise reduction and optimized dehazing processes. Prior to the primary dehazing step, noise in hazy images is minimized using homomorphic processing, Contrast-Limited Adaptive Histogram Equalization (CLAHE), and a fast-dehazing method, which together serve as enhancement tools. This preprocessing ensures that noise is not amplified during dehazing, resulting in superior image and video quality compared to existing methods without preprocessing. The primary dehazing step employs an optimized technique leveraging a haze model to compute the transmission map as a ratio of radiance and reflectance components, while atmospheric light is estimated using quadtree operations. The inclusion of a fast-dehazing method further enhances the quality, particularly in near-infrared (NIR) frames. We evaluated the proposed technique using real hazy images, visible frames, and NIR frames. Performance metrics such as Peak Signal-to-Noise Ratio (PSNR), correlation, and entropy demonstrate the efficacy of the proposed technique over the optimized dehazing method alone and other state-of-the-art approaches. Comparative studies highlight that the proposed technique consistently produces higher-quality dehazed images and frames, with NIR frames showing significant improvement over visible frames. The proposed technique achieves enhancement percentages of 23.48% and 202% for visible and NIR videos, respectively, establishing itself as a robust solution for haze removal.</p>