Abstract: Image registration is a fundamental problem in computer vision, and the epipolar geometry constraint is the most popular model in it, which usually suffers from the efficiency problem and accuracy problem in applications.Therefore, this work focuses on the model designing problem in image registration.Firstly, by analyzing the characteristics of the mapping between corresponding points and the geometric properties of the surface formed by corresponding points in 4-dimentional joint image space, we introduced the concept of correspondence manifold (CM) .Compared with the available method, the advantage of this definition is independent of the continuity hypothesis.Secondly, the concept correspondence view (CV) is introduced to describe the CM after studying the characteristics and completeness of the point mapping in CM and its upward views, based on which the estimation of CM can be converted into a regression problem.Finally, we compared the CM with the commonly used model epipolar geometry constraint theoretically and experimentally.The theoretical result is that CM is a sufficient and necessary constraint in correspondence problem, but epipolar geometry is a necessary but insufficient one.The experimental results on real images show that CM-based mismatch removing method is more accurate, robust and efficient than that based on epipolar geometry constraint.