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The proposed methodology learns the optimal model parameters by optimizing a cost function, which is defined over the expected output and the actual result. Moreover, binary cross-entropy function [36], is used as given in Equation (2.2), to quantify the prediction accuracy. Here θ denotes the parameters of the model. The symbols xi, xj. and yi,j represent the input image, reference image and the expected output, respectively. The output of the function F increases if the reference and the test images are equal. Otherwise, the function tries to decrease the value. The Adam optimizer [37], is used to optimize this cost function.

(2.2)