Constraints provide an important means of incorporating a priori information into the image restoration process. However, much of the information available for constructing constraints is of a tentative nature. If the validity of this tentative information can be assessed before it is incorporated into the solution, helpful constraints can be retained while harmful ones can be discarded. Cross validation is introduced as a technique for assessing the validity of such constraint sets. Because the full cross validation procedure is computationally burdensome, a modification is suggested that allows a more feasible implementation without substantially sacrificing the performance of the full procedure. Experimental results demonstrate the excellent performance of both the full and modified procedures.