论文标题
使用塔克分解
Single Image Super-Resolution of Noisy 3D Dental CT Images Using Tucker Decomposition
论文作者
论文摘要
张量分解已被证明是各种3D图像处理任务中的强大工具，例如denoising和super-lasolution。在这种情况下，我们最近提出了单个图像超分辨率（SISR）的基于规范的多核分解（CPD）算法。该算法已显示出比流行优化技术快的数量级。在这项工作中，我们研究了塔克分解带来的附加值。虽然CPD允许联合实施SISR模型的脱氧和反卷积步骤，但随着Tucker分解，DeNoising首先实现，其次是反卷积。这样，由噪声引起的反卷积的不良性得到部分缓解。比较了使用两种不同的张量分解技术实现的结果，并研究了针对噪声的鲁棒性。为了验证，我们使用了牙科图像。提出的方法的优越性以峰值信噪比，结构相似性指数，运河分割的准确性和运行时的形式显示。
Tensor decomposition has proven to be a strong tool in various 3D image processing tasks such as denoising and super-resolution. In this context, we recently proposed a canonical polyadic decomposition (CPD) based algorithm for single image super-resolution (SISR). The algorithm has shown to be an order of magnitude faster than popular optimization-based techniques. In this work, we investigated the added value brought by Tucker decomposition. While CPD allows a joint implementation of the denoising and deconvolution steps of the SISR model, with Tucker decomposition the denoising is realized first, followed by deconvolution. This way the ill-posedness of the deconvolution caused by noise is partially mitigated. The results achieved using the two different tensor decomposition techniques were compared, and the robustness against noise was investigated. For validation, we used dental images. The superiority of the proposed method is shown in terms of peak signal-to-ratio, structural similarity index, the canal segmentation accuracy, and runtime.
