Optimizing Image Fusion Using Wavelet Transform Based Alternative Direction Multiplier Method

Image fusion is the method of merge two or more photographs in such a manner that the best features of each are preserved. When a panchromatic picture is combined with multispectral data, the intended outcome is an image that has both the panchromatic image's spatial quality and resolution as well as the multispectral image's spectral resolution and quality. Standard digital fusion methods are generally successful in infusing spatial detail into multispectral data, but the colour information often distorted in the process. Wavelet transformations have been used in picture fusion for over a decade, and there has been a lot of study done on them. The findings of a variety of wavelet-based image fusion strategies are examined in this research, which includes a framework of Discrete Wavelet Transform (DWT) and Undecimated Discrete Wavelet Transform (UDWT) theory, as well as the Alternative Direction Multiplier Method (ADMM) fusion approach. Wavelet-based schemes have been proven to perform better than conventional schemes in general, especially when it comes to minimising colour distortion. Standard approaches combined with wavelet transformations yield better outcomes than standard methods or basic wavelet-based methods alone. More advanced models for infusing detailed description can enhance the outcomes of wavelet-based approaches; however, these schemes frequently have higher set-up requirements.