Context-Aware Seam Restoration for Image Extension

A seam is a set of pixels with minimum energy forming a continuous line in an image. By eliminating or duplicating seams iteratively, an input image can be retargeted. However, this process often results in blurring, stretching, or distortion problems around the seams, especially when extending a target image. We propose a novel approach for image extension using content-aware seam restoration to solve this problem. First, we design CSR-Net, which employs features from the horizontal region of target pixels to restore the seams. Second, we develop an image extension scenario based on the seam restoration and the training methodology of CSR-Net. Experimental results demonstrate that the proposed algorithm provides more accurate expanded results at seam pixels the seams than conventional algorithms.