This paper presents a new wavelet-based multi-focus image fusion approach using method noise and anisotropic diffusion for two separate cases, i.e., with and without a reference image. It is specifically designed for real-time surveillance applications. It is a multi-step image fusion approach. Firstly, stationary wavelet transform (SWT) is performed to get low and high-frequency coefficients. Secondly, the input images' LL bands are fused using average operation. The rest of the respective bands are fused using a new correlation coefficient (CC) based fusion strategy using the threshold value calculated by structural similarity index metric (SSIM). Then inverse SWT is performed to reconstruct the fused coefficients. Thirdly, anisotropic diffusion-based method noise thresholding is introduced to recover the unprocessed and still damaged input images' components. Finally, the proposed approach's performance has experimented with various qualitative (visual perception) and quantitative factors (performance metrics). The experimental outcomes show that the proposed approach generates fine edges, high visual quality, high clarity of objects, and less degradation. The proposed multi-step hybrid technique is implemented to generate high-quality fused images. The experimental outcomes verify the achievement of the proposed approach.

本文提出了一种新的基于小波的多焦点图像融合方法，该方法在两种情况下（即带有参考图像和不带有参考图像的情况下）使用了噪声和各向异性扩散方法。它是专为实时监视应用程序而设计的。这是一种多步骤图像融合方法。首先，进行平稳小波变换（SWT）以获得低频和高频系数。其次，使用平均运算来融合输入图像的LL波段。使用基于新的相关系数（CC）的融合策略，使用结构相似性指标度量（SSIM）计算的阈值，融合其余各个波段。然后，执行逆SWT以重建融合系数。第三，引入了基于各向异性扩散的方法噪声阈值化，以恢复未处理且仍损坏的输入图像的成分。最后，所提出的方法的性能已经在各种定性（视觉感知）和定量因素（性能指标）上进行了实验。实验结果表明，所提出的方法产生了精细的边缘，高的视觉质量，物体的高清晰度和较少的退化。实现了所提出的多步混合技术以生成高质量的融合图像。实验结果验证了所提出方法的成功。实验结果表明，所提出的方法产生了精细的边缘，高的视觉质量，物体的高清晰度和较少的退化。实现了所提出的多步混合技术以生成高质量的融合图像。实验结果验证了所提出方法的成功。实验结果表明，所提出的方法产生了精细的边缘，高的视觉质量，物体的高清晰度和较少的退化。实现了所提出的多步混合技术以生成高质量的融合图像。实验结果验证了所提出方法的成功。