论文标题
图像通过K-SVD具有原始偶偶有的活动算法的图像deno
Image denoising via K-SVD with primal-dual active set algorithm
论文作者
论文摘要
K-SVD算法已成功应用于图像deNo的任务数十年，但速度和准确性的大瓶颈仍然需要注意才能破裂。对于涉及$ \ ell_ {0} $约束的K-SVD中的稀疏编码阶段，普遍的方法通常会贪婪地寻求近似的解决方案，但一旦噪声水平高，效果就较小。替代$ \ ell_ {1} $优化被证明比$ \ ell_ {0} $强大，但是，时间消耗可以阻止其实现。在本文中，我们通过将Primal-Dual Active Set（PDAS）算法应用于它，提出了一个名为K-SVD $ _P $的新的K-SVD框架。不同于贪婪算法的K-SVD，K-SVD $ _P $算法制定了由KKT（Karush-Kuhn-Tucker）促进的选择策略，并在稀疏编码阶段的有效更新中产生了屈服。由于K-SVD $ _P $算法寻求在此denoising问题中使用简单的显式表达的偶性问题的等效解决方案，因此可以同时达到denoising的速度和质量。进行实验，并证明我们的K-SVD $ _p $与最新方法相当的脱索性能。
K-SVD algorithm has been successfully applied to image denoising tasks dozens of years but the big bottleneck in speed and accuracy still needs attention to break. For the sparse coding stage in K-SVD, which involves $\ell_{0}$ constraint, prevailing methods usually seek approximate solutions greedily but are less effective once the noise level is high. The alternative $\ell_{1}$ optimization is proved to be powerful than $\ell_{0}$, however, the time consumption prevents it from the implementation. In this paper, we propose a new K-SVD framework called K-SVD$_P$ by applying the Primal-dual active set (PDAS) algorithm to it. Different from the greedy algorithms based K-SVD, the K-SVD$_P$ algorithm develops a selection strategy motivated by KKT (Karush-Kuhn-Tucker) condition and yields to an efficient update in the sparse coding stage. Since the K-SVD$_P$ algorithm seeks for an equivalent solution to the dual problem iteratively with simple explicit expression in this denoising problem, speed and quality of denoising can be reached simultaneously. Experiments are carried out and demonstrate the comparable denoising performance of our K-SVD$_P$ with state-of-the-art methods.
