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We formulate model of kernel regression process which is exploited for image upsampling. The probabilistic process is studied for estimating missing information in an image. The term 'linear regression' is a designing tool for the relationship between a scalar dependent variable 'b' and one or more explanatory variables denoted 'A. ' We provide some results of regression method that are tested on two natural images. Simulation results compare performance with various condition and parameterdoi:10.14257/ijseia.2015.9.5.01 fatcat:kiruf5aisvf53gv4tt37wyldwe