像素
人工智能
降噪
非本地手段
计算机科学
水准点(测量)
噪音(视频)
计算机视觉
模式识别(心理学)
图像(数学)
图像去噪
地理
大地测量学
作者
Yingkun Hou,Jun Xu,Mingxia Liu,Guanghai Liu,Li Liu,Fan Zhu,Ling Shao
标识
DOI:10.1109/tip.2020.2980116
摘要
Non-local self similarity (NSS) is a powerful prior of natural images for image denoising. Most of existing denoising methods employ similar patches, which is a patch-level NSS prior. In this paper, we take one step forward by introducing a pixel-level NSS prior, i.e., searching similar pixels across a non-local region. This is motivated by the fact that finding closely similar pixels is more feasible than similar patches in natural images, which can be used to enhance image denoising performance. With the introduced pixel-level NSS prior, we propose an accurate noise level estimation method, and then develop a blind image denoising method based on the lifting Haar transform and Wiener filtering techniques. Experiments on benchmark datasets demonstrate that, the proposed method achieves much better performance than previous non-deep methods, and is still competitive with existing state-of-the-art deep learning based methods on real-world image denoising. The code is publicly available at https://github.com/njusthyk1972/NLH.
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