Parallax-induced diplopia (double vision) is a phenomenon deeply rooted in the human perceptual system. Here, users perceive the same object twice, at slightly different locations. This effect worsens, the larger the distance between the object and the point the user fixates upon. While diplopia serves as an important depth cue, it is detrimental for various interaction techniques in virtual reality. Paralign, a novel method for diplopia mitigation, is introduced. Paralign removes the double vision by employing eye-tracking-based horopter estimation and a one-eyed image shift to establish retinal correspondence for individual objects. A user study (N = 16) compared Paralign against other diplopia mitigation strategies as well as uncorrected stereo rendering. The evaluation showed similar performance as other strategies and a clear user preference for Paralign over stereo rendering. Further, the feasibility and versatility of Paralign is demonstrated in multiple image plane based applications.