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MITIGATING DISCONTINUITIES IN SEGMENTED KARHUNEN-LOEVE TRANSFORMS

机译:缓解Karhunen-Loeve变换的不连续性

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The Karhunen-Loeve Transform (KLT) is a popular transform used in multiple image processing scenarios. Sometimes, the application of the KLT is not carried out as a single transform over an entire image. Rather, the image is divided into smaller spatial regions (segments), each of which is transformed by a smaller dimensional KLT. Such a situation may penalize the transform efficiency. An improvement for the segmented KLT, aiming at mitigating discontinuities arising on the edge of adjacent regions, is proposed in this paper. In the case of moderately varying image regions, discontinuities occur as the consequence of disregarded similarity between transform domains, as the order and sign of eigenvectors in the transform matrices are mismatched. In the proposed method, the KLT is adjusted to guarantee the best achievable similarity via the optimal assignment and sign correspondence for eigenvectors. Experimental results indicate that the proposed transform improves the similarity between transform domains, and reduces RMSE on the edge of adjacent regions. In consequence, images processed by the adjusted KLT present better cohesion and continuity between independently transformed regions.
机译:在Karhunen-Loeve变换(KLT)是一种流行的在多个图像处理场景变换使用。有时,KLT的应用并不作为一个单一的变换在整个图像中进行。更确切地说,将图像分割成更小的空间区域(段),其中的每一个由一较小的维KLT转化。这种情况可能惩罚转换效率。对于分割的KLT的改进,目的是在相邻区域的边缘所产生的不连续性减轻,在本文提出。在适度的变化图像区域的情况下,不连续发生作为变换域之间豁免计算相似度的结果,作为订单和不匹配的变换矩阵本征向量的登录。在所提出的方法中,KLT被调整,以保证通过最佳分配和符号对应于特征向量的最佳实现的相似性。实验结果表明,所提出的变换提高变换域之间的相似性,并降低了对相邻区域的边缘RMSE。结果,图像由独立转化的区域之间的调整KLT本更好的凝聚力和连续性处理。

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