No abstract

No abstract

Image fusion is viewed as perhaps the best procedure to confine the level of uncertainty and convey a significant feeling of picture lucidity. It is a strategy of combining the appropriate information/data from a group of pictures into a solitary resultant (intertwined) picture that would render higher picture proficiency and clarity. Until now, the image fusion procedures looked like Discrete Wavelet Transform (DWT) or pixel-based methodologies. These already established methods have limited effectiveness. Also, they fail to deliver the typical outcomes like edge perseverance, spatial resolution, and shift-invariance. To get rid of these demerits, in this paper, we have proposed a hybrid approach called Principal Component Stationary Wavelet Transform (PC-SWT) that combines Principal Component Analysis (PCA) and Stationary Wavelet Transform. SWT is an algorithm that defines the wavelet transformation to compensate for the absence of translation invariance in DWT. PCA is a methodical approach that utilizes an orthogonal transformation in order to transform a group of perceptions of possibly correlated values into the principal components, which are linearly uncorrelated variables. When compared to conventional methods, PC-SWT intends to obtain a more efficient, clear, and superior quality image. This fused image is expected to have all of its preserved edges as well as its spatial resolution. In addition to this, it can also be used to deal with shift-invariance.

No abstract