  In interpretation of remote sensing images, it is possible that some images
which are supplied by different sensors become incomprehensible. For better
visual perception of these images, it is essential to operate series of
pre-processing and elementary corrections and then operate a series of main
processing steps for more precise analysis on the images. There are several
approaches for processing which are depended on the type of remote sensing
images. The discussed approach in this article, i.e. image fusion, is the use
of natural colors of an optical image for adding color to a grayscale satellite
image which gives us the ability for better observation of the HR image of OLI
sensor of Landsat-8. This process with emphasis on details of fusion technique
has previously been performed; however, we are going to apply the concept of
the interpolation process. In fact, we see many important software tools such
as ENVI and ERDAS as the most famous remote sensing image processing tools have
only classical interpolation techniques (such as bi-linear (BL) and
bi-cubic/cubic convolution (CC)). Therefore, ENVI- and ERDAS-based researches
in image fusion area and even other fusion researches often dont use new and
better interpolators and are mainly concentrated on the fusion algorithms
details for achieving a better quality, so we only focus on the interpolation
impact on fusion quality in Landsat-8 multispectral images. The important
feature of this approach is to use a statistical, adaptive, and edge-guided
interpolation method for improving the color quality in the images in practice.
Numerical simulations show selecting the suitable interpolation techniques in
MRF-based images creates better quality than the classical interpolators.
