Effective enhancement method of low-light-level images based on the guided filter and multi-scale fusion

被引:7
|
作者
Lang, Yi-zheng [1 ]
Qian, Yun-sheng [1 ]
Kong, Xiang-yu [1 ]
Zhang, Jing-zhi [1 ]
Wang, Yi-lun [1 ]
Cao, Yang [1 ]
机构
[1] Nanjing Univ Sci & Technol, Sch Elect & Opt Engn, Nanjing 210094, Jiangsu, Peoples R China
关键词
CONTRAST ENHANCEMENT;
D O I
10.1364/JOSAA.468876
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
Aiming to solve the problem of low-light-level (LLL) images with dim overall brightness, uneven gray distribution, and low contrast, in this paper, we propose an effective LLL image enhancement method based on the guided filter and multi-scale fusion for contrast enhancement and detail preservation. First, a base image and detail image(s) are obtained by using the guided filter. After this procedure, the base image is processed by a maximum entropybased Gamma correction to stretch the gray level distribution. Unlike the existing methods, we enhance the detail image(s) based on the guided filter kernel, which reflects the image area information. Finally, a new method is proposed to generate a sequence of artificial images to adjust the brightness of the output, which has a better performance in image detail preservation compared with other single-input algorithms. Experiments show that the proposed method can provide a more significant performance in enhancing contrast, preserving details, and maintaining the natural feeling of the image than the state of the art. (c) 2022 Optica Publishing Group
引用
收藏
页码:1 / 9
页数:9
相关论文
共 50 条
  • [1] Low-light-level image enhancement based on fusion and Retinex
    Shao, Wenbo
    Liu, Lei
    Jiang, Jiawei
    Yan, Yifan
    JOURNAL OF MODERN OPTICS, 2020, 67 (13) : 1190 - 1196
  • [2] A color fusion method of infrared and low-light-level images based on visual perception
    Han, Jing
    Yan, Minmin
    Zhang, Yi
    Bai, Lianfa
    INTERNATIONAL SYMPOSIUM ON OPTOELECTRONIC TECHNOLOGY AND APPLICATION 2014: IMAGE PROCESSING AND PATTERN RECOGNITION, 2014, 9301
  • [3] Low Light Image Enhancement Based on Multi-Scale Network Fusion
    Liu, Xuan
    Zhang, Chenfeng
    Wang, Yingzhi
    Ding, Kai
    Han, Tailin
    Liu, Hong
    Tian, Yu
    Xu, Bo
    Ju, Mingchi
    IEEE ACCESS, 2022, 10 : 127853 - 127862
  • [4] Effective multi-scale enhancement fusion method for low-light images based on interest-area perception OCTM and "pixel healthiness" evaluation
    Wang, Yi-lun
    Lang, Yi-zheng
    Qian, Yun-sheng
    VISUAL COMPUTER, 2025, 41 (04): : 2607 - 2627
  • [5] Color Fusion Method for Low-Light-Level and Infrared Images in Night Vision
    Yang, Shaokui
    Liu, Wen
    Deng, Chan
    Zhang, Xin
    Yang, Shaokui
    Deng, Chan
    2012 5TH INTERNATIONAL CONGRESS ON IMAGE AND SIGNAL PROCESSING (CISP), 2012, : 534 - 537
  • [6] An Image Enhancement Method Based on Multi-scale Fusion
    Wang, Haoming
    ARTIFICIAL INTELLIGENCE AND ROBOTICS, ISAIR 2022, PT I, 2022, 1700 : 37 - 42
  • [7] Fusion of the low-light-level visible and infrared images for night-vision context enhancement
    Zhu, Jin
    Jin, Weiqi
    Li, Li
    Han, Zhenghao
    Wang, Xia
    CHINESE OPTICS LETTERS, 2018, 16 (01)
  • [8] Fusion of the low-light-level visible and infrared images for night-vision context enhancement
    朱进
    金伟其
    李力
    韩正昊
    王霞
    Chinese Optics Letters, 2018, 16 (01) : 94 - 99
  • [9] MULTI-SCALE FEATURE GUIDED LOW-LIGHT IMAGE ENHANCEMENT
    Guo, Lanqing
    Wan, Renjie
    Su, Guan-Ming
    Kot, Alex C.
    Wen, Bihan
    2021 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), 2021, : 554 - 558
  • [10] Mosaic ceramic surface defect detection enhancement algorithm based on guided filter and multi-scale fusion
    Dong, Guanping
    You, Rui
    Liu, Sai
    Wu, Nanshou
    Kong, Xiangyu
    Huang, Pingnan
    Fan, Wenting
    Wang, Zixi
    MATERIALS TESTING, 2025, 67 (03) : 568 - 578