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We present a novel solution to the warping recovery problem. Our algorithm has several distinct advantages; it is scalable, it enables effective integration of boundary and continuity constraints, and most importantly it is computationally much less demanding than the previous approaches. In addition, our algorithm accurately detects non-linear warping functions without being restricted to the linearity assumptions and 2-D planar deformations unlike the existing methods. We achieve to formulate the image warping as an optimization process in 1-D scan-line search spaces. We construct the search spaces from block-matching based image distances, and then we traverse minimum cost paths into these search spaces using boundary conditions to determine the horizontal and vertical components of warping for each pixel. Our experiments prove the performance of the proposed algorithm.