基于稀疏信号处理的椒盐噪声去除
Salt-and-Pepper Noise Removal Based on Sparse Signal Processing
AI总结：
本文提出一种基于稀疏信号处理的椒盐噪声去除方法，将去噪转化为稀疏重建问题，引入高效重建算法，在PSNR和视觉效果上优于现有方法，并可用于擦除信道缺失样本重建。
摘要：
本文提出了一种从图像中去除椒盐噪声的新方法。现有的大多数方法基于有序统计滤波器，而我们的方法基于稀疏信号处理这一新兴理论。换言之，我们将去噪问题转化为稀疏信号重建问题，并利用相应的技术加以处理。因此，我们的方法输出的图像避免了其他大多数方法所存在的不理想的不透明度缺陷。我们还引入了一种高效的重建算法，用于我们的方法中。仿真结果表明，我们的方法在峰值信噪比（PSNR）和视觉标准方面均优于其他最知名的方法。此外，我们的方法可以轻松用于擦除信道中缺失样本的重建。
英文摘要：
In this paper, we propose a new method for Salt-and-Pepper noise removal from images. Whereas most of the existing methods are based on Ordered Statistics filters, our method is based on the growing theory of Sparse Signal Processing. In other words, we convert the problem of denoising into a sparse signal reconstruction problem which can be dealt with the corresponding techniques. As a result, the output image of our method is preserved from the undesirable opacity which is a disadvantage of most of the other methods. We also introduce an efficient reconstruction algorithm which will be used in our method. Simulation results indicate that our method outperforms the other best-known methods both in term of PSNR and visual criterion. Furthermore, our method can be easily used for reconstruction of missing samples in erasure channels.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
