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
  • [31] Low-light-level image enhancement algorithm based on integrated networks
    Wang, Peng
    Wu, Jiao
    Wang, Haiyan
    Li, Xiaoyan
    Yang, Yongxia
    MULTIMEDIA SYSTEMS, 2022, 28 (06) : 2015 - 2025
  • [32] Low-light-level image enhancement algorithm based on integrated networks
    Peng Wang
    Jiao Wu
    Haiyan Wang
    Xiaoyan Li
    Yongxia Yang
    Multimedia Systems, 2022, 28 : 2015 - 2025
  • [33] VESSEL ENHANCEMENT WITH MULTI-SCALE AND CURVILINEAR FILTER MATCHING FOR PLACENTA IMAGES
    Chang, Jen-Mei
    Huynh, Nen
    Vasquez, Marilyn
    Salafia, Carolyn
    Bucker, Barbara
    Thorp, John
    Mittal, Sandhya
    PLACENTA, 2013, 34 (09) : A73 - A73
  • [34] Enhancement of Underwater Images by Integrating a Multi-scale Median Filter and JND
    Zhang, Hong
    He, Chao-ran
    Xiang, Ji-hua
    Yuan, Guo-qiang
    Guo, Peng-wei
    2015 INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND TECHNOLOGY (ICCST 2015), 2015, : 200 - 205
  • [35] Infrared and Low-light-level Image Fusion Method Based on Sparse Representation and Color Transfer
    Xu, Shihong
    Huang, Guoqing
    Liu, Cunchao
    Xiong, Chunping
    SENSORS, MECHATRONICS AND AUTOMATION, 2014, 511-512 : 462 - +
  • [36] An underwater image enhancement method based on multi-scale layer decomposition and fusion
    Yang, Jie
    Wang, Jun
    SIGNAL PROCESSING, 2025, 227
  • [37] A Multi-Scale Fusion and Transformer Based Registration Guided Speckle Noise Reduction for OCT Images
    Tan, Zhiwei
    Shi, Fei
    Zhou, Yi
    Wang, Jingcheng
    Wang, Meng
    Peng, Yuanyuan
    Xu, Kai
    Liu, Ming
    Chen, Xinjian
    IEEE TRANSACTIONS ON MEDICAL IMAGING, 2024, 43 (01) : 473 - 488
  • [38] Infrared and Low-light-level Visible Image Fusion Algorithm Based on Contrast Enhancement and Cauchy Fuzzy Function
    Jiang Ze-tao
    He Yu-ting
    Zhang Shao-qin
    ACTA PHOTONICA SINICA, 2019, 48 (06)
  • [39] Pansharpening based on convolutional autoencoder and multi-scale guided filter
    Ahmad AL Smadi
    Shuyuan Yang
    Zhang Kai
    Atif Mehmood
    Min Wang
    Ala Alsanabani
    EURASIP Journal on Image and Video Processing, 2021
  • [40] Pansharpening based on convolutional autoencoder and multi-scale guided filter
    AL Smadi, Ahmad
    Yang, Shuyuan
    Kai, Zhang
    Mehmood, Atif
    Wang, Min
    Alsanabani, Ala
    EURASIP JOURNAL ON IMAGE AND VIDEO PROCESSING, 2021, 2021 (01)