A Retinex-Based Variational Model for Enhancement and Restoration of Low-Contrast Remote-Sensed Images Corrupted by Shot Noise

被引：8
|
作者：
Febin, I. P. [1 ]
Jidesh, P. [1 ]
Bini, A. A. [2 ]
机构：
[1] Natl Inst Technol Karnataka, Dept Math & Computat Sci, Surathkal 575025, India
[2] Indian Inst Informat Technol, Kottayam 686635, Kerala, India
来源：
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING | 2020年 / 13卷
关键词：
Contrast enhancement; denoising; perceptual image processing; variational method; HISTOGRAM EQUALIZATION; FRAMEWORK; VISION;
D O I：
10.1109/JSTARS.2020.2975044
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Remotely sensed images are widely used in many imaging applications. Images captured under adverse atmospheric conditions lead to degraded images that are contrast deficient and noisy. This study is intended to address these defects of remotely sensed data efficiently. A perceptually inspired variational model is designed based upon the Bayesian framework, powered by the retinex theory. The atmospheric noise or the shot noise (precisely following a Poisson distribution) and contrast inhomogeneity are addressed in this article. The model thus designed is tested and verified both visually and quantitatively using various test data under different statistical measures. The comparative study reveals the efficiency of the model.
引用
收藏
页码：941 / 949
页数：9
相关论文
共 38 条
  • [1] RETINEX-BASED PERCEPTUAL CONTRAST ENHANCEMENT IN IMAGES USING LUMINANCE ADAPTATION
    Xu, Kaiqiang
    Jung, Cheolkon
    2017 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2017, : 1363 - 1367
  • [2] Retinex-Based Perceptual Contrast Enhancement in Images Using Luminance Adaptation
    Fu, Qingtao
    Jung, Cheolkon
    Xu, Kaiqiang
    IEEE ACCESS, 2018, 6 : 61277 - 61286
  • [3] Joint Retinex-based variational model and CLAHE-in-CIELUV for enhancement of low-quality color retinal images
    Huang, Zongheng
    Tang, Chen
    Xu, Min
    Lei, Zhenkun
    APPLIED OPTICS, 2020, 59 (28) : 8628 - 8637
  • [4] A Novel Retinex-Based Fractional-Order Variational Model for Images With Severely Low Light
    Gu, Zhihao
    Li, Fang
    Fang, Faming
    Zhang, Guixu
    IEEE TRANSACTIONS ON IMAGE PROCESSING, 2020, 29 : 3239 - 3253
  • [5] A Retinex-based variational model for noise suppression and nonuniform illumination correction in corneal confocal microscopy images
    Han, Rui
    Tang, Chen
    Xu, Min
    Lei, Zhenkun
    PHYSICS IN MEDICINE AND BIOLOGY, 2023, 68 (02):
  • [6] Retinex-Based Variational Framework for Low-Light Image Enhancement and Denoising
    Ma, Qianting
    Wang, Yang
    Zeng, Tieyong
    IEEE TRANSACTIONS ON MULTIMEDIA, 2023, 25 : 5580 - 5588
  • [7] RETINEX-BASED LOW-LIGHT HYPERSPECTRAL RESTORATION USING CAMERA RESPONSE MODEL
    Liu, Na
    Wang, Yinjian
    Yang, Yixiao
    Li, Wei
    Tao, Ran
    2022 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2022), 2022, : 3323 - 3326
  • [8] Variational model for contrast enhancement of remote sensing images based on perception and gradient domain
    College of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing
    211101, China
    不详
    210096, China
    Dongnan Daxue Xuebao, 6 (1051-1056):
  • [9] Enhancement of low-contrast images by internal noise-induced Fourier coefficient rooting
    Rajlaxmi Chouhan
    P. K. Biswas
    R. K. Jha
    Signal, Image and Video Processing, 2015, 9 : 255 - 263
  • [10] Enhancement of low-contrast images by internal noise-induced Fourier coefficient rooting
    Chouhan, Rajlaxmi
    Biswas, P. K.
    Jha, R. K.
    SIGNAL IMAGE AND VIDEO PROCESSING, 2015, 9 : 255 - 263
1234