论文标题
增强线性逆问题的快速迭代收缩阈值算法
Enhanced Fast Iterative Shrinkage Thresholding Algorithm For Linear Inverse Problem
论文作者
论文摘要
线性逆问题来自各种现实世界中的应用，例如图像脱毛，indpainting等，这些应用仍然是图像质量改进的推力研究领域。在本文中，我们引入了一种新算法，称为线性反问题的增强型快速迭代收缩阈值算法（EFISTA）。该算法使用加权最小二项和正则化参数的缩放版本来加速目标函数最小化。图像去除模拟结果表明，在峰值信号与噪声比（PSNR）方面，尤其是在高噪声水平上，Efista具有较高的执行速度，其性能的提高了。通过这些激励结果，我们可以说，提出的EFISTA也可能有助于其他线性反问题，以提高重建速度并有效地处理噪声。
The linear inverse problem emerges from various real-world applications such as Image deblurring, inpainting, etc., which are still thrust research areas for image quality improvement. In this paper, we have introduced a new algorithm called the Enhanced fast iterative shrinkage thresholding algorithm (EFISTA) for linear inverse problems. This algorithm uses a weighted least square term and a scaled version of the regularization parameter to accelerate the objective function minimization. The image deblurring simulation results show that EFISTA has a superior execution speed, with an improved performance than its predecessors in terms of peak-signal-to-noise ratio (PSNR), particularly at a high noise level. With these motivating results, we can say that the proposed EFISTA can also be helpful for other linear inverse problems to improve the reconstruction speed and handle noise effectively.
