论文标题
实时图像通过迭代最小二乘
Real-time Image Smoothing via Iterative Least Squares
论文作者
论文摘要
对于许多计算机视觉和图形应用程序，边缘保留图像平滑是一个基本过程。平滑质量和处理速度之间存在一个权衡：高平滑质量通常需要高计算成本，从而导致较低的处理速度。在本文中，我们提出了一种新的基于全局优化的方法，称为迭代最小二乘（ILS），以进行有效的边缘披露图像平滑。我们的方法可以产生高质量的结果，但计算成本要低得多。全面的实验表明，提出的方法可以产生几乎没有可见伪影的结果。此外，IL的计算可以高度平行，可以通过多线程计算或GPU硬件轻松加速IL。随着GTX 1080 GPU的加速，它可以以20fps的颜色图像的速率和47fps的速率处理1080p分辨率（$ 1920 \ times1080 $）的图像。此外，ILS是灵活的，可以修改以处理需要不同平滑属性的更多应用。几种应用的实验结果表明了该方法的有效性和效率。该代码可在\ url {https://github.com/wliusjtu/real time-image-image-smoothing-via-iterative-least-squares}中获得
Edge-preserving image smoothing is a fundamental procedure for many computer vision and graphic applications. There is a tradeoff between the smoothing quality and the processing speed: the high smoothing quality usually requires a high computational cost which leads to the low processing speed. In this paper, we propose a new global optimization based method, named iterative least squares (ILS), for efficient edge-preserving image smoothing. Our approach can produce high-quality results but at a much lower computational cost. Comprehensive experiments demonstrate that the propose method can produce results with little visible artifacts. Moreover, the computation of ILS can be highly parallel, which can be easily accelerated through either multi-thread computing or the GPU hardware. With the acceleration of a GTX 1080 GPU, it is able to process images of 1080p resolution ($1920\times1080$) at the rate of 20fps for color images and 47fps for gray images. In addition, the ILS is flexible and can be modified to handle more applications that require different smoothing properties. Experimental results of several applications show the effectiveness and efficiency of the proposed method. The code is available at \url{https://github.com/wliusjtu/Real-time-Image-Smoothing-via-Iterative-Least-Squares}
