Academic Journal of Computing & Information Science, 2023, 6(9); doi: 10.25236/AJCIS.2023.060917.
Xiaoting Niu, Guanghai Zheng
School of Software, Dalian Jiaotong University, Dalian, China
Low illumination images have insufficient local and global light exposure, loss of structural and detail information, and are prone to generating a large amount of noise. The overall image is grayish or even completely dark, and people often cannot recognize the content of the image with the naked eye. Image enhancement technology aims to enhance image brightness, adjust image contrast, restore hidden details in the Fark, and enhance the utilization value of images through corresponding technical means.The traditional low illumination image enhancement methods mainly focus on Histogram equalization and Retinex methods. Based on the drawbacks of the traditional methods, this paper studies the low illumination image enhancement algorithm based on Convolutional neural network, builds a mathematical model, and lays the foundation for subsequent experimental research and application.
Convolutional Neural Network, CNN, Low Illumination Image, Enhancement Algorithm, Evaluation Index, Dilation Convolution, Loss Function, Network Optimization
Xiaoting Niu, Guanghai Zheng. Research on Low Illumination Image Enhancement Algorithm Based on Convolutional Neural Network. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 9: 115-121. https://doi.org/10.25236/AJCIS.2023.060917.
 Kumar A M, Sekhar C P. A Review Paper on Low Light Image Enhancement Methods for Un-uniform Illumination[J]. IFAC PapersOnLine, 2022, 55(10): 287-292.
 Suresh T, Domnic S. Deep learning model for person identification and verification on low illumination images[P]. National Institute of Technology,Department of Computer Applications, Tiruchirappalli, India, 2022, 31: 062009-062009.
 Rasheed Muhammad Tahir, Guiyu G,Daming S, et al. An Empirical Study on Retinex Methods for Low-Light Image Enhancement[J]. Remote Sensing,2022,14(18): 4608-4608.
 B. W. Li. Research on Enhancement Algorithms of Low Illumination Images Based on Convolutional Neural Networks[D]. Nanjing University of Posts and Telecommunications, 2021.
 Gil Eddie M, Steelman Zachary A, Sedelnikova Anna, Bixler Joel N. Comparing the segmentation of quantitative phase images of neurons using convolutional neural networks trained on simulated and augmented imagery[J]. Neurophotonics, 2023, 10(3): 035004-035004.
 Y. Qin. Research on Denoising and Enhancement of Low light Image Based on Convolutional Neural Network[D]. Chang’an University, 2019.
 Shapovalova S, Moskalenko Yu, Baranichenko O. Increasing the Receptive Field of Neurons in Convolutional Neural Networks [J]. Cybernetics and Systems Analysis, 2023, 59(2): 339-345.
 K. J. Wang. Research on Low-light Image Enhancement based on Convolutional Neural Network [D]. Guilin University of Electronic Technology, 2022.
 Houssaine E H, NourEddine J, Mohamed E. A deep learning framework for time series classification using normal cloud representation and convolutional neural network optimization[J]. Computational Intelligence, 2022, 38(6): 2056-2074.