Intelligent Image Enhancement Method for Micro Manipulator Industrial Robot
Author: 
Jingyu ChenAuthors Info & Claims
Jingyu ChenAuthors Info & ClaimsPages 591 - 595
Abstract
In order to improve the precision of micro-manipulating industrial robots working in complex background, an image intelligent enhancement method is proposed in this paper. Based on the structure of micromanipulation micro- vision system, a weighted guided filtering Retinex algorithm is used for image enhancement in low illumination environment. Moreover, dynamic range adjustment is set up to realize intelligent enhancement. Experimental results show that the proposed method is superior to the contrast method in that the average image enhancement is higher than 80, standard deviation is higher than 70 and entropy is higher than 7.
References
[1]
Zhang Z, Wang X, Liu J, et al. Robotic micromanipulation: Fundamentals and applications[J]. Annual Review of Control, Robotics, and Autonomous Systems, 2019, 2: 181--203.
[2]
Fontana G, Ruggeri S, Legnani G, et al. Unconventional calibration strategies for micromanipulation work-cells[J]. Robotica, 2018, 36(12): 1897--1919.
[3]
Bučinskas V, Subačiūtė-Žemaitienė J, Dzedzickis A, et al. Robotic micromanipulation: a) actuators and their application[J]. Robotic systems and applications, 2021, 1(1): 2--23.
[4]
Venkatesan V, Cappelleri D J. Path planning and micromanipulation using a learned model[J]. IEEE Robotics and Automation Letters, 2018, 3(4): 3089--3096.
[5]
Jain R K, Majumder S, Ghosh B, et al. Analysis of multiple robotic assemblies by cooperation of multimobile micromanipulation systems (M 4 S)[J]. The International Journal of Advanced Manufacturing Technology, 2017, 91(9): 3033--3050.
[6]
Seon J A, Dahmouche R, Gauthier M. Enhance in-hand dexterous micromanipulation by exploiting adhesion forces[J]. IEEE Transactions on Robotics, 2017, 34(1): 113--125.
[7]
Li H, Zhu B, Zhang X, et al. Pose sensing and servo control of the compliant nanopositioners based on microscopic vision[J]. IEEE Transactions on Industrial Electronics, 2020, 68(4): 3324--3335.
[8]
Su G, Lu G, Yan P. Planar motion measurement of a compliant micro stage: An enhanced microscopic vision approach[J]. IEEE Transactions on Instrumentation and Measurement, 2019, 69(5): 1930--1939.
[9]
Song W, Song Z J, Zhang Y N, et al. High-Precision Target Pose Adjustment Based on 3-CCD Microscope Vision System[J]. Opt. Precis. Eng, 2018, 26: 1794--1801.
[10]
Rodríguez J A M. Microscope self-calibration based on micro laser line imaging and soft computing algorithms[J]. Optics and Lasers in Engineering, 2018, 105: 75--85.
[11]
Apolinar J, Rodríguez M. Three-dimensional microscope vision system based on micro laser line scanning and adaptive genetic algorithms[J]. Optics Communications, 2017, 385: 1--8.
[12]
Wei J, Zhijie Q, Bo X, et al. A nighttime image enhancement method based on Retinex and guided filter for object recognition of apple harvesting robot[J]. International Journal of Advanced Robotic Systems, 2018, 15(1): 1729881417753871.
[13]
Wen S, Hu X, Ma J, et al. Autonomous robot navigation using Retinex algorithm for multiscale image adaptability in low-light environment[J]. Intelligent Service Robotics, 2019, 12(4): 359--369.
[14]
Li M, Liu J, Yang W, et al. Structure-revealing low-light image enhancement via robust retinex model[J]. IEEE Transactions on Image Processing, 2018, 27(6): 2828--2841.
[15]
Shi Y, Wu X, Zhu M. Low-light image enhancement algorithm based on retinex and generative adversarial network[J]. arXiv preprint arXiv:1906.06027, 2019.
[16]
Zhou J, Zhang D, Zou P, et al. Retinex-based laplacian pyramid method for image defogging[J]. IEEE Access, 2019, 7: 122459--122472.
[17]
Zhuang P, Ding X. Underwater image enhancement using an edge-preserving filtering Retinex algorithm[J]. Multimedia Tools and Applications, 2020: 1--21.
[18]
Wei J, Zhijie Q, Bo X, et al. A nighttime image enhancement method based on Retinex and guided filter for object recognition of apple harvesting robot[J]. International Journal of Advanced Robotic Systems, 2018, 15(1): 1729881417753871.
[19]
Zhang S, Wang T, Dong J, et al. Underwater image enhancement via extended multi-scale Retinex[J]. Neurocomputing, 2017, 245: 1--9.
[20]
Gao Y, Hu H M, Li B, et al. Naturalness preserved nonuniform illumination estimation for image enhancement based on retinex[J]. IEEE Transactions on Multimedia, 2017, 20(2): 335--344.
Index Terms
- Intelligent Image Enhancement Method for Micro Manipulator Industrial Robot
Recommendations
A contrast enhancement method using dynamic range separate histogram equalization
In this paper, a contrast enhancement method using dynamic range separate histogram equalization (DRSHE) is proposed. Histogram equalization (HE) method is widely used for contrast enhancement. However, HE is not suitable for consumer electronic ...
An Image Enhancement Approach Using Retinex and YIQ
ITCS '09: Proceedings of the 2009 International Conference on Information Technology and Computer Science - Volume 01On a basis of Retinex and YIQ color space, a new approach of color image enhancement is presented in this paper. The original image in the RGB color space is converted to the YIQ color space, and the multi-scale Retinex (MSR) algorithm is introduced to ...
Image enhancement algorithm based on Retinex for small-bore steel tube butt weld's X-ray imaging
It is very common to use X-ray digital detection for the Small-bore steel tube butt weld. But the X-ray images have some shortcomings such as that contrast is not high, background noise is big, and the details can not be shown obviously, these ...
Comments
Information & Contributors
Information
Published In
October 2021
1723 pages
ISBN:9781450384322
DOI:10.1145/3501409
Copyright © 2021 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 31 December 2021
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
EITCE 2021
EITCE 2021: 2021 5th International Conference on Electronic Information Technology and Computer Engineering
October 22 - 24, 2021
Xiamen, China
Acceptance Rates
EITCE '21 Paper Acceptance Rate 294 of 531 submissions, 55%;
Overall Acceptance Rate 508 of 972 submissions, 52%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 28Total Downloads
- Downloads (Last 12 months)4
- Downloads (Last 6 weeks)0
Reflects downloads up to 06 Nov 2024
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in