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.
Improvement of Image Fusion by Integrating Wavelet Transform with Principal Component Analysis
