基于最小位移可视差的连续Seam Carving算法在图像缩放中的研究

图像缩放技术要求对图像缩放的同时保证重要信息不丢失且物体边缘不发生扭曲。近年来，Seam Carving及其改进算法得到了广泛的关注和研究。由于采用了离散式最小能量线迭代搜索策略，缩放信息无法在迭代过程中传递导致扭曲现象普遍存在。该文针对上述问题提出最小位移可视差(JND)检测算法，能够有效地检测每一次迭代中出现的潜在扭曲信息。能量权重\begin{document}$ {E}_{w} $\end{document}能够将JND信息累加传递给后续的迭代过程，从而抑制缩放过程中的边缘扭曲现象。通过JND算法和能量权重，该文首次将离散的Seam Carving模型转变为连续缩放模型。最后，在公共数据集RetargetMe上与最新的图像缩放算法进行多组对比实验，验证了所提方法的有效性和先进性。

Continuous Seam Carving Algorithm Based on Just Noticeable Distortion Algorithm in Image Retargeting

Image retargeting technologies require important information preservation and less edge distortion during increasing/decreasing image size. The seam carving based algorithms, as the classic retargeting model, receive widespread attention in recent years. However, because of the discrete least energy seam searching strategy, the retargeting information can not be passed generation by generation, which causes retargeting distortions to prevail. To solve this problem, the Just Noticeable Distortion (JND) algorithm is proposed to detect the potential distribution of distortion information. Through the proposed energy weight Ew, the JND information can be passed to the following retargeting iteration for distortion reduction. According to the best knowledge, it is the first time to propose the seam carving algorithm in continuous way by the JND algorithm and energy weight, are the promising results also demonstrated compared with several new approaches at public database ‘Retarget Me’, qualitatively and quantitatively.