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基于直方图均衡化和双边滤波的艺术图像特征增强方法

The feature enhancement method of artistic images based on histogram equalization and bilateral filtering.

作者信息

Zhang Wenjing

机构信息

Art School, Zhengzhou University of Science and Technology, Zhengzhou, China.

出版信息

PeerJ Comput Sci. 2024 Jun 5;10:e2109. doi: 10.7717/peerj-cs.2109. eCollection 2024.

DOI:10.7717/peerj-cs.2109
PMID:39669451
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11637003/
Abstract

To improve the rendering effect of artistic images, a method enhancing features of artistic images is proposed based on histogram equalization and bilateral filtering in the article. Firstly, artistic images are divided into both high and low-frequency representations, and the multi-step enhancement processing level is delimited by multi-band decomposition. Secondly, the noise in the image is removed by bilateral filtering. Then, the grey-level histogram of the image is modified by using the histogram equalization. Finally, the features of the artistic image are enhanced by global tone mapping after histogram equalization processing is conducted. Then, the image is sharpened to improve the enhancement effect further. The experiments show that the features of the color and edge details turn out to be more vivid and clearer after the proposed method is implemented. The structural similarity (SSIM) measure of the image increases to 0.973, and the average gradient gets close to 0.8, which shows that the proposed method is effective.

摘要

为了提高艺术图像的渲染效果，本文提出了一种基于直方图均衡化和双边滤波的增强艺术图像特征的方法。首先，将艺术图像分解为高频和低频表示，并通过多波段分解来界定多步增强处理级别。其次，利用双边滤波去除图像中的噪声。然后，使用直方图均衡化修改图像的灰度直方图。最后，在进行直方图均衡化处理后，通过全局色调映射增强艺术图像的特征。接着，对图像进行锐化以进一步提高增强效果。实验表明，实施该方法后，颜色和边缘细节特征变得更加生动和清晰。图像的结构相似性（SSIM）度量增加到0.973，平均梯度接近0.8，这表明该方法是有效的。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c532/11637003/1a8e071160cb/peerj-cs-10-2109-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c532/11637003/88644c947720/peerj-cs-10-2109-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c532/11637003/36703d298895/peerj-cs-10-2109-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c532/11637003/91072820b89d/peerj-cs-10-2109-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c532/11637003/1a8e071160cb/peerj-cs-10-2109-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c532/11637003/88644c947720/peerj-cs-10-2109-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c532/11637003/36703d298895/peerj-cs-10-2109-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c532/11637003/91072820b89d/peerj-cs-10-2109-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c532/11637003/1a8e071160cb/peerj-cs-10-2109-g004.jpg

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本文引用的文献

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Dark2Light: multi-stage progressive learning model for low-light image enhancement.从暗到亮：用于低光照图像增强的多阶段渐进学习模型。
Opt Express. 2023 Dec 18;31(26):42887-42900. doi: 10.1364/OE.507966.
2
Robust underwater image enhancement with cascaded multi-level sub-networks and triple attention mechanism.基于级联多级子网和三重注意力机制的稳健水下图像增强
Neural Netw. 2024 Jan;169:685-697. doi: 10.1016/j.neunet.2023.11.008. Epub 2023 Nov 10.
3
Industrial x-ray image enhancement network based on a ray scattering model.基于射线散射模型的工业X射线图像增强网络
Appl Opt. 2023 Jul 10;62(20):5526-5537. doi: 10.1364/AO.493750.
4
Motion illusion-like patterns extracted from photo and art images using predictive deep neural networks.基于预测性深度神经网络从照片和艺术图像中提取运动错觉样模式。
Sci Rep. 2022 Mar 10;12(1):3893. doi: 10.1038/s41598-022-07438-3.