论文标题
Geodesic Gramian denoising应用于被不同概率分布采样的噪声污染的图像
Geodesic Gramian Denoising Applied to the Images Contaminated With Noise Sampled From Diverse Probability Distributions
论文作者
论文摘要
随着Quotidian使用复杂的相机的使用，现代社会中的人们更感兴趣地捕获优质的图像。但是，由于图像中的噪音污染，图像的质量可能不如人们的期望。因此，在保留重要图像特征的同时滤除噪声是必不可少的要求。当前现有的denoising方法对对污染噪声进行采样的概率分布有自己的假设，以实现其预期的DeNoising绩效。在本文中，我们利用我们最近的基于Gramian的过滤方案去除从所选图像中的五个突出概率分布中采样的噪声。该方法通过采用从图像而不是像素分区的斑块来保留图像平滑度，并通过在贴片空间而不是在图像域中执行降级来保留重要的图像特征。我们使用三个基准计算机视觉测试图像验证了其降解性能，该图像应用于两种最先进的denoising方法，即BM3D和K-SVD。
As quotidian use of sophisticated cameras surges, people in modern society are more interested in capturing fine-quality images. However, the quality of the images might be inferior to people's expectations due to the noise contamination in the images. Thus, filtering out the noise while preserving vital image features is an essential requirement. Current existing denoising methods have their own assumptions on the probability distribution in which the contaminated noise is sampled for the method to attain its expected denoising performance. In this paper, we utilize our recent Gramian-based filtering scheme to remove noise sampled from five prominent probability distributions from selected images. This method preserves image smoothness by adopting patches partitioned from the image, rather than pixels, and retains vital image features by performing denoising on the manifold underlying the patch space rather than in the image domain. We validate its denoising performance, using three benchmark computer vision test images applied to two state-of-the-art denoising methods, namely BM3D and K-SVD.
