Edge detail enhancement algorithm for high-dynamic range images

IF 2.1 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Lanfei Zhao, Qidan Zhu
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引用次数: 0

Abstract

Abstract Existing image enhancement methods have problems of a slow data transmission and poor conversion effect, resulting in a low image-recognition rate and recognition efficiency. To solve these problems and improve the recognition accuracy and recognition efficiency of image features, this study proposes an edge detail enhancement algorithm for a high-dynamic range image. The original image is transformed by Fourier transform, and the low-frequency and high-frequency images are obtained by the frequency-domain Gaussian filtering and inverse Fourier transform. The low-frequency image is processed by the contrast limited adaptive histogram equalization, and the high-frequency image is obtained by the nonsharpening masking and gray transformation. The low-frequency enhanced and the high-frequency enhanced images are weighted and fused to enhance the edge details of the image. Finally, the experimental results show that the proposed high-dynamic range image edge detail enhancement algorithm maintains the image recognition rate of more than 80% during the practical application, and the recognition time is within 1,200 min, which enhances the image effect, improves the recognition accuracy and recognition efficiency of image characteristics, and fully meets the research requirements.
高动态范围图像边缘细节增强算法
现有的图像增强方法存在数据传输速度慢、转换效果差等问题,导致图像识别率和识别效率较低。为了解决这些问题,提高图像特征的识别精度和识别效率,本研究提出了一种针对高动态范围图像的边缘细节增强算法。对原始图像进行傅里叶变换,通过频域高斯滤波和傅里叶反变换得到低频和高频图像。低频图像采用对比度有限的自适应直方图均衡化处理,高频图像采用非锐化掩模和灰度变换处理。对低频增强图像和高频增强图像进行加权融合,增强图像的边缘细节。最后,实验结果表明,所提出的高动态范围图像边缘细节增强算法在实际应用过程中保持了80%以上的图像识别率,识别时间在1200 min以内,增强了图像效果,提高了图像特征的识别精度和识别效率,完全满足了研究要求。
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来源期刊
Journal of Intelligent Systems
Journal of Intelligent Systems COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
5.90
自引率
3.30%
发文量
77
审稿时长
51 weeks
期刊介绍: The Journal of Intelligent Systems aims to provide research and review papers, as well as Brief Communications at an interdisciplinary level, with the field of intelligent systems providing the focal point. This field includes areas like artificial intelligence, models and computational theories of human cognition, perception and motivation; brain models, artificial neural nets and neural computing. It covers contributions from the social, human and computer sciences to the analysis and application of information technology.
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