Research on adaptive enhancement of robot vision image based on multi-scale filter

IF 1.8 Q3 REMOTE SENSING

International Journal of Image and Data Fusion Pub Date : 2022-11-23 DOI:10.1080/19479832.2022.2149630

Qin Dong

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引用次数: 1

Abstract

ABSTRACT Contrast enhancement and histogram equalisation are two image enhancement methods, which can lead to changes in the edge position of the resulting image, blurring or even loss of details. Therefore, this paper introduces a multi-scale filter to adaptively enhance the robot visual image, improve the brightness of the robot visual image, enrich the image details and reduce the image enhancement time. According to Retinex theory, the characteristic information of robot visual image is obtained, the logarithmic domain operation form of Retinex algorithm is obtained, the robot visual reflection image of high-frequency part is determined, the robot illumination visual image is estimated by multiscale filter, and the scale constant of Gaussian filter is obtained; According to the Retinex algorithm of weighted guided filtering, the robot visual image enhancement process is designed. The experimental results show that the average value of the robot visual image enhanced by this method is 88.63, the standard deviation is 62.78, the information entropy is 8.18, the robot visual image enhancement time is only 5.9s, and the PSNR of the robot visual image is up to 39.92, which proves that the robot visual image enhancement effect of this method is good.

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基于多尺度滤波的机器人视觉图像自适应增强研究

对比度增强和直方图均衡化是两种图像增强方法，这两种方法都会导致图像边缘位置发生变化，使图像模糊甚至细节丢失。因此，本文引入一种多尺度滤波器对机器人视觉图像进行自适应增强，提高机器人视觉图像的亮度，丰富图像细节，减少图像增强时间。根据Retinex理论，获得了机器人视觉图像的特征信息，得到了Retinex算法的对数域运算形式，确定了机器人视觉反射图像的高频部分，用多尺度滤波估计了机器人照明视觉图像，得到了高斯滤波的尺度常数;根据加权引导滤波的Retinex算法，设计了机器人视觉图像增强过程。实验结果表明，该方法增强的机器人视觉图像均值为88.63，标准差为62.78，信息熵为8.18，机器人视觉图像增强时间仅为5.9s，机器人视觉图像的PSNR高达39.92，证明了该方法的机器人视觉图像增强效果良好。

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来源期刊

International Journal of Image and Data Fusion REMOTE SENSING-

CiteScore

5.00

自引率

0.00%

发文量

期刊介绍： International Journal of Image and Data Fusion provides a single source of information for all aspects of image and data fusion methodologies, developments, techniques and applications. Image and data fusion techniques are important for combining the many sources of satellite, airborne and ground based imaging systems, and integrating these with other related data sets for enhanced information extraction and decision making. Image and data fusion aims at the integration of multi-sensor, multi-temporal, multi-resolution and multi-platform image data, together with geospatial data, GIS, in-situ, and other statistical data sets for improved information extraction, as well as to increase the reliability of the information. This leads to more accurate information that provides for robust operational performance, i.e. increased confidence, reduced ambiguity and improved classification enabling evidence based management. The journal welcomes original research papers, review papers, shorter letters, technical articles, book reviews and conference reports in all areas of image and data fusion including, but not limited to, the following aspects and topics: • Automatic registration/geometric aspects of fusing images with different spatial, spectral, temporal resolutions; phase information; or acquired in different modes • Pixel, feature and decision level fusion algorithms and methodologies • Data Assimilation: fusing data with models • Multi-source classification and information extraction • Integration of satellite, airborne and terrestrial sensor systems • Fusing temporal data sets for change detection studies (e.g. for Land Cover/Land Use Change studies) • Image and data mining from multi-platform, multi-source, multi-scale, multi-temporal data sets (e.g. geometric information, topological information, statistical information, etc.).