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Pixel-Level Non-local Image Smoothing With Objective Evaluation

delete2021-01-01
delete33
PRE
AI
J
Jun Xu
Z
Zhiang Liu
Y
Yingkun Hou
X
Xiantong Zhen
L
Ling Shao
M
Ming‐Ming Cheng *
DOI:10.1109/TMM.2020.3037535delete
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摘要

摘要

En 中文
Recently, imagesmoothing has gained increasing attention due to its prerequisite role in other image processing tasks, e.g., image enhancement and editing. However, the evaluation of image smoothing algorithms is usually performed by subjective observation on images without corresponding ground truths. To promote the development of image smoothing algorithms, in this paper, we construct a novel Nankai Smoothing (NKS) dataset containing 200 images blended by versatile structure images and natural textures. The structure images are inherently smooth and naturally taken as ground truths. On our NKS dataset, we comprehensively evaluate 14 popular image smoothing algorithms. Moreover, we propose a Pixel-level Non-Local Smoothing (PNLS) method to well preserve the structure of the smoothed images, by exploiting the pixel-level non-local self-similarity prior of natural images. Extensive experiments on several benchmark datasets demonstrate that our PNLS outperforms previous algorithms on the image smoothing task. Ablation studies also reveal the work mechanism of our PNLS on image smoothing. To further show its effectiveness, we apply our PNLS on several applications such as semantic region smoothing, detail/edge enhancement, and image abstraction. The dataset and code are available at https://github.com/zal0302/PNLS.
Keyword:
Smoothing methods
Task analysis
Measurement
Benchmark testing
Image edge detection
Visualization
Optimization
Image smoothing
benchmark dataset
performance evaluation
pixel-level non-local self similarity
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期刊

IEEE Transactions on Multimedia 封面图
IEEE Transactions on Multimedia
IF:
9.7
论文数:
4.5K
被引数:
2.4W

机构

T
Taishan University
学者数:
669
论文数: 710
被引数: 1.1K
N
nankai university
学者数:
4.8W
论文数: 3.3W
被引数: 74
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