A coarse-to-fine perspective distortion minimization algorithm is proposed for image repairing based on an additional large displacement view (LDV) of the same scene. It works by correcting the perspective distortion in the LDV image, and then utilizing the rectified LDV image to recover the missing areas on the target image. First, under the assumption of a planar scene, the LDV image is globally warped according to a homography to generate the initial distortion correction. Second, a mismatch recognition mechanism detects the remaining distortions in the initially corrected LDV image. They are further relaxed by energy optimization of overlap correspondences with the expectations of color constancy and displacement field smoothness. Third, under the constraints of epipolar geometry, displacement field smoothness and color consistency among the neighboring pixels, the missing pixels are orderly restored according to a specially-defined repairing priority function. Poisson image blending is adopted to eliminate the ghost effect between the repaired region and its surroundings and get the seamless repairing effect. Experimental results demonstrate that this method outperforms recent state-of-the-art image completion algorithms, especially for completing large damaged area with complex structure information.