A new wavelet-based multi-focus image fusion technique using method noise and anisotropic diffusion for real-time surveillance application

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.