1. A coefficient learning apparatus comprising: regression coefficient calculation means for obtaining, from an image of a first signal, a tap in which a plurality of linear feature amounts corresponding to a pixel of interest are used as elements and for calculating a regression coefficient for regression prediction computation that obtains a value of a pixel corresponding to the pixel of interest in an image of a second signal through a product-sum computation of each of the elements of the tap and the regression coefficient; regression prediction value calculation means for performing the regression prediction computation on the basis of the calculated regression coefficient and the tap obtained from the image of the first signal and calculating a regression prediction value; discrimination information assigning means for assigning, to the pixel of interest, discrimination information for determining whether the pixel of interest is a pixel belonging to a first discrimination class or to a second discrimination class on the basis of the result of the comparison between the calculated regression prediction value and the value of the pixel corresponding to the pixel of interest in the image of the second signal; discrimination coefficient calculation means for obtaining, from the image of the first signal, a tap in which a plurality of linear feature amounts corresponding to the pixel of interest are used as elements on the basis of the assigned discrimination information and for calculating the discrimination coefficient for a discrimination prediction computation that obtains a discrimination prediction value for identifying a discrimination class to which the pixel of interest belongs through a product-sum computation of each of the elements of the tap and the discrimination coefficient; discrimination prediction value calculation means for performing the discrimination prediction computation on the basis of the calculated discrimination coefficient and the tap obtained from the image of the first signal; and classification means for classifying each of the pixels of the image of the first signal into one of the first discrimination class and the second discrimination class on the basis of the calculated discrimination prediction value, wherein the regression coefficient calculation means further calculates the regression coefficient using only the pixels classified as the first discrimination class and further calculates the regression coefficient using only the pixel classified as the second discrimination class.