Fast Single Image Haze Removal Method Based on the Adaptive Gain and Minimum Channel

Single-image haze removal algorithms restore clear images from hazy inputs but often struggle with high computational complexity and halos or artifacts from patch-based priors. We propose a novel non-local patch-based haze removal algorithm that estimates atmospheric light and scene transmission using the minimum image channel. This approach reduces reliance on patch-based priors, ensuring efficient and high-quality dehazing. An adaptive gain, based on the ratio of $I_{min}$ and $I_{max}$ , adjusts the minimum channel to refine atmospheric light, scene depth, and transmission estimates, addressing inaccuracies in traditional methods. Histogram equalization and image multiplication enhance scene radiance, improving contrast, color accuracy, and naturalness under challenging haze conditions. Subjective evaluations confirm our method achieves the highest MOS, consistently rated most visually appealing. In addition, objective metrics show our method outperforms state-of-the-art techniques, achieving better FADE, NIQE, SSEQ, BRISQUE, and PIQE scores by 2% to 13%, indicating more natural colors, minimal distortion, and superior restoration quality. Additionally, our method demonstrates the lowest computational cost and processes 8K images up to 21 times faster than the second-fastest method, CAP. This breakthrough makes the algorithm ideal for real-time applications in consumer electronics such as smartphones and digital cameras, delivering high-quality, haze-free imagery with minimal computational cost.