图像自适应中基于形变控制的细缝剪裁方法

Seam Carving Algorithm for Image Adaptation Based on Distortion Control

  • 摘要: 为了降低图像自适应过程中图像内容缺失和图像扭曲变形,文章中提出了一种基于形变控制的图像自适应细缝裁剪方法,将融合局部和全局显著性的显著图作为细缝裁剪参考的能量图,对需要剪裁的细缝按照这个能量图进行排序,以更好地保护图像中感兴趣区域(ROI),保留图像的主要内容信息;同时,利用SIFT流矢量场来衡量剪裁图像的形变程度,每移除一定数量的细缝就计算剪裁图像与原始图像之间的形变,一旦形变达到某一阈值,就停止细缝裁剪,转换为均匀缩放使图像到达目标尺寸。实验结果表明,文章中提出的方法更好地平衡了均匀缩放和非均匀剪裁,更有利于保留图像主要内容和避免主要内容的变形。

     

    Abstract: To reduce the information loss and geometric distortion of image retargeting, a novel seam carving method for image retargeting based on image distortion control was proposed. In the proposed method, a saliency map combining local and global contrast saliency was used as the energy map for sorting out the seams need to be carved based on attention saliency, which can preserve the region of interest (ROI) better. Meanwhile, the dense SIFT flow was utilized to measure the degree of image distortion during seam carving. During seam carving, each time a certain number of seams were removed, and the distortion degree between the original image and the resized image was calculated. Once the distortion reached a certain threshold, seam carving was terminated and switched to scaling the image to the required size. The experimental results demonstrate that the proposed method keep a good balance between seam carving and scaling for image retargeting and is better at preserving the main content information and avoiding the distortion of key objects.

     

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