The paper proposes a fast and effective image fusion method that combines multiple images into a highly informative fused image. The method uses a two-scale decomposition to separate the images into base layers, which capture large-scale intensity variations, and detail layers, which capture small-scale details. A novel guided filtering-based weighted average technique is introduced to leverage spatial consistency for fusing these layers. The guided filter is applied to construct weight maps that reflect pixel saliency and spatial context, ensuring that similar pixels have similar weights. Experimental results demonstrate that the proposed method outperforms state-of-the-art fusion approaches in terms of preserving useful information, avoiding artifacts, and robustness to mis-registration and noise. The method is also computationally efficient, making it suitable for real-world applications.The paper proposes a fast and effective image fusion method that combines multiple images into a highly informative fused image. The method uses a two-scale decomposition to separate the images into base layers, which capture large-scale intensity variations, and detail layers, which capture small-scale details. A novel guided filtering-based weighted average technique is introduced to leverage spatial consistency for fusing these layers. The guided filter is applied to construct weight maps that reflect pixel saliency and spatial context, ensuring that similar pixels have similar weights. Experimental results demonstrate that the proposed method outperforms state-of-the-art fusion approaches in terms of preserving useful information, avoiding artifacts, and robustness to mis-registration and noise. The method is also computationally efficient, making it suitable for real-world applications.