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In interpretation of remote sensing images, it is possible that some imageswhich are supplied by different sensors become incomprehensible. For bettervisual perception of these images, it is essential to operate series ofpre-processing and elementary corrections and then operate a series of mainprocessing steps for more precise analysis on the images. There are severalapproaches for processing which are depended on the type of remote sensingimages. The discussed approach in this article, i.e. image fusion, is the useof natural colors of an optical image for adding color to a grayscale satelliteimage which gives us the ability for better observation of the HR image of OLIsensor of Landsat-8. This process with emphasis on details of fusion techniquehas previously been performed; however, we are going to apply the concept ofthe interpolation process. In fact, we see many important software tools suchas ENVI and ERDAS as the most famous remote sensing image processing tools haveonly classical interpolation techniques (such as bi-linear (BL) andbi-cubic/cubic convolution (CC)). Therefore, ENVI- and ERDAS-based researchesin image fusion area and even other fusion researches often dont use new andbetter interpolators and are mainly concentrated on the fusion algorithmsdetails for achieving a better quality, so we only focus on the interpolationimpact on fusion quality in Landsat-8 multispectral images. The importantfeature of this approach is to use a statistical, adaptive, and edge-guidedinterpolation method for improving the color quality in the images in practice.Numerical simulations show selecting the suitable interpolation techniques inMRF-based images creates better quality than the classical interpolators.

MRF-Based Multispectral Image Fusion Using an Adaptive Approach Based on Edge-Guided Interpolation