论文标题
通过双侧随机预测，颜色图像降级的限制限制的低排四元素近似
Constrained low-rank quaternion approximation for color image denoising by bilateral random projections
论文作者
论文摘要
在这封信中，我们提出了一种新型的低级别四元素近似（LRQA）模型，直接在有效消除颜色图像中的噪声方面直接限制四个季度等级。 LRQA模型可以全面地处理颜色图像，而不是独立于颜色空间组件，因此可以完全利用RGB通道之间的高相关性。我们通过使用四边形双侧随机投影（Q-BRP）来设计一种迭代算法来有效地优化所提出的模型。 Q-BRP的主要优点是，低级别四元基质的近似值可以以廉价的方式准确地获得。此外，彩色图像denoising进一步基于非本地自相似性（NSS）。对颜色图像的实验结果表明了该方法的有效性和优越性。
In this letter, we propose a novel low-rank quaternion approximation (LRQA) model by directly constraining the quaternion rank prior for effectively removing the noise in color images. The LRQA model treats the color image holistically rather than independently for the color space components, thus it can fully utilize the high correlation among RGB channels. We design an iterative algorithm by using quaternion bilateral random projections (Q-BRP) to efficiently optimize the proposed model. The main advantage of Q-BRP is that the approximation of the low-rank quaternion matrix can be obtained quite accurately in an inexpensive way. Furthermore, color image denoising is further based on nonlocal self-similarity (NSS) prior. The experimental results on color image denoising illustrate the effectiveness and superiority of the proposed method.
