A Comprehensive Review of Improvement of Image Contrast in Case of Poor Light
P. Mishra1 , A. Sinhal2 , D.S. Tomar3
Section:Review Paper, Product Type: Journal Paper
Volume-2 ,
Issue-5 , Page no. 168-175, May-2014
Online published on May 31, 2014
Copyright © P. Mishra, A. Sinhal, D.S. Tomar . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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IEEE Style Citation: P. Mishra, A. Sinhal, D.S. Tomar, “A Comprehensive Review of Improvement of Image Contrast in Case of Poor Light,” International Journal of Computer Sciences and Engineering, Vol.2, Issue.5, pp.168-175, 2014.
MLA Style Citation: P. Mishra, A. Sinhal, D.S. Tomar "A Comprehensive Review of Improvement of Image Contrast in Case of Poor Light." International Journal of Computer Sciences and Engineering 2.5 (2014): 168-175.
APA Style Citation: P. Mishra, A. Sinhal, D.S. Tomar, (2014). A Comprehensive Review of Improvement of Image Contrast in Case of Poor Light. International Journal of Computer Sciences and Engineering, 2(5), 168-175.
BibTex Style Citation:
@article{Mishra_2014,
author = {P. Mishra, A. Sinhal, D.S. Tomar},
title = {A Comprehensive Review of Improvement of Image Contrast in Case of Poor Light},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {5 2014},
volume = {2},
Issue = {5},
month = {5},
year = {2014},
issn = {2347-2693},
pages = {168-175},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=181},
publisher = {IJCSE, Indore, INDIA},
}
RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=181
TI - A Comprehensive Review of Improvement of Image Contrast in Case of Poor Light
T2 - International Journal of Computer Sciences and Engineering
AU - P. Mishra, A. Sinhal, D.S. Tomar
PY - 2014
DA - 2014/05/31
PB - IJCSE, Indore, INDIA
SP - 168-175
IS - 5
VL - 2
SN - 2347-2693
ER -
VIEWS | XML | |
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Abstract
In this paper, we are reviewing several research papers regarding study and analysis towards improvement of image contrast in case of poor light. In this paper we most focus on many algorithms that has been designed for enhancement of image, At the end, a study has been made by comparing all the proposed parameters that with certain advantages and having limitations too, that have been conducted a relevant experimental analysis to evaluate both their robustness and their performance. Our review work involves a comparative study of Improvement of Image Contrast for image enrichment with respect to the following parameter Performance, Scalability, Image enhancement, Image Acquisition, Applying Morphological operators, Detecting and extracting the background, Applying contrast enhancement operators:- block analysis and opening by reconstruction, Applying image enhancement techniques like image sharpening etc.
Key-Words / Index Term
: Digital Image Processing, Denoiser, Morphological Operators, Filters, Image contrast, Image segmentation
References
[1] E. Peli, �Contrast in complex images�, Optical Engineering, Volume 7, No.10, Page No (2032�2040), 1990.
[2] C. R. Gonz�lez, E.Woods, �Digital Image Processing�, Englewood Cliffs, National Journal: Prentice Hall, Page No 197-199, 1992.
[3] G. M. Matheron. �Random sets and integral in Geometry�, Wiley, New York, 288 pages, 1975.
[4] J. Serra. �Image Analysis Using Mathematical Morphology�, Conference Automatic Face and Gesture Recognition, 1982.
[5] R. H. Sherrier and G. A. Johnson, �Regionally Adaptive Histogram Equalization of the Chest,� IEEE Transaction on Medical Imaging, Volume Medical Imaging - 6, Page No.1�7, 1987.
[6] Lixu Gu, Toyohisa Kaneko, �Morphological Segmentation Applied to Character Extraction from Color Cover Images�, Fourth International Symposium on Mathematical, Page No. 367-374,1998.
[7] A. Toet, �Multiscale Contrast Enhancement with Applications to Image Fusion,� Optical Engineering, Volume 31, No. 5, 1992.
[8] P. Salembier and J. Serra, �Flat Zones Filtering, Connected Operators and Filters by Reconstruction,� IEEE Transaction Image Process, Volume 3, No.8, Page No. 1153�1160, Aug. 1995.
[9] N.J. B. McFarlane, C.P. Schofield, �Segmentation And Tracking Of Piglets In Images�, Machine Vision and Applications, Volume 8, No. 3, Page No 187�193, 1995.
[10] S. Mukhopadhyay, B. Chanda, �A Multiscale Morphological Approach to Local Contrast Enhancement,� Signal Process, Volume 80, No. 4, Page No 685�696, 2000.
[11] I.R. Terol-Villalobos, �Morphological Image Enhancement and Segmentation,� in Advances in Imaging and Electron Physics, Page No. 207�273, 2001.
[12] J. Kasperek, �Real Time Morphological Image Contrast Enhancement in Virtex FPGA,� in Lecture Notes in Computer Science Volume 2147, Page No 430-440, 2001.
[13] J. Short, J. Kittler, and K. Messer, �A Comparison of Photometric Normalization Algorithms for Face Verification,� presented at the IEEE International Conference Automatic Face and Gesture Recognition, 2004.
[14] I.R.Terol-Villalobos, �Morphological Connected Contrast Mappings Based on Top-Hat Criteria: A Multiscale Contrast Approach,� Optical Engineering, Volume 43, No. 7, Page No 1577�1595, 2004.
[15] Hee-Won Lee and Byung-Uk Lee, �Improving Brightness for a Multi-projector Display Considering Image Content,� in Lecture Notes in Computer Science, Volume 4292, Page No 70 � 78, 2006.
[16] S. Calderara, R. Melli, A. Prati, R. Cucchiara, �Reliable Back Ground Suppression For Complex Scenes�, in: ACM International Workshop on Video Surveillance and Sensor Networks, 2006.
[17] Mannan S.M., Aamir Saeed Malik, Humaira Nisar, and Tae-Sun Choi, �Rectification of Illumination in Images used for Shape�, in Lecture Notes in Computer Science, Volume 4292, Page No 166 � 175, 2006.
[18] Dongil Han and Byoungmoo Lee, �Development of Early Tunnel Fire Detection Algorithm Using the Image Processing�, in Lecture Notes in Computer Science 4292, Page No 39 � 48, 2006.
[19] Michael A. Webster, Kyle McDermott, and George Bebis, �Fitting the World to the Mind: Transforming Images to Mimic Perceptual Adaptation�, in Lecture Notes in Computer Science 4841, Page No 757�768, 2007.
[20] J. Yao, J. marc Odobez, �Multi-Layer Background Subtraction Based on Color and Texture�, In IEEE The Computer Vision and Pattern Recognition Conference Visual Surveillance Workshop, 2007.
[21] Ehsan Adeli Mosabbeb, Maryam Sadeghi, Mahmoud Fathy , �A New Approach for Vehicle Detection in Congested Traffic Scenes Based on Strong Shadow Segmentation�, in Lecture Notes in Computer Science Volume 4842, pp. 427�436, 2007.
[22] Charles Kervrann, J�r�me Boulanger, �Local Adaptivity to Variable Smoothness for Exemplar-Based Image Regularization and Representation�, International Journal of Computer Vision-Liquid-Liquid Chromatography, pp. 45�69, 2008.
[23] Ang�lica R. Jim�nez-S�nchez, Jorge D. Mendiola- Santiba�ez, �Morphological Background Detection and Enhancement of Images With Poor Lighting�, IEEE transactions on image processing, Volume 18, No. 3, March 2009.
[24] Yu-Tung Kuol, Wen-Hsiang Tsai1, �A New 3D Imaging System Using a Portable Two-Camera Omni-Imaging Device for Construction and Browsing of Human-Reachable Environments,� in Lecture Notes in Computer Science Volume 6938, pp. 484�495, 2010.
[25] Yang Chen, Deepak Khosla, et al, �A Neuromorphic Approach to Object Detection and Recognition in Airborne Videos with Stabilization,� in Lecture Notes in Computer Science Volume 6939, pp. 126�135, 2011.
[26] Pablo Arias , Gabriele Facciolo , et al �A Variational Framework for Exemplar-Based Image Inpainting,� Springer Science+Business Media, LLC, International Journal of Computer Vision-Liquid-Liquid Chromatography, Volume 93, pp. 319�347, 2011.
[27] Saibabu Arigela and Vijayan K. Asari, �Adaptive and Nonlinear Techniques for Visibility Improvement of Hazy Images, in Lecture Notes in Computer Science Volume 6939, pp. 75�84, 2011.
[28] Vishnukumar Galigekere, Gutemberg Guerra-Filho, �A Synthesis-and-Analysis Approach to Image Based Lighting�, in Lecture Notes in Computer Science Volume 7431, pp. 292�304, 2012.
[29] M. Hofmann, P. Tiefenbacher, G. Rigoll, �Background Segmentation With Feedback: The Pixel-Based Adaptive Segmenter�, in: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 38�43, 2012.
[30] Norhayati Bakri , Ratnawati Ibrahim, et al, �Linking Mathematics and Image Processing Through Common Terminologies�, Social and Behavioral Sciences Volume 102, pp. 454 � 463, 2013.
[31] Farah Yasmin Abdul Rahman, Aini Hussain, et al, �Enhancement of Background Subtraction Techniques Using a Second Derivative in Gradient Direction Filter�, Journal of Electrical and Computer Engineering, Volume 2013, Article ID 598708, pp. 1-12, 2013.
[32] Laurent Navarro, Guang Deng, Guy Courbebaisse, �The Symmetric Logarithmic Image Processing Model,� Digital Signal Processing, Volume 23, pp. 1337�1343, 2013.
[33] Aur�lien Ducournau, Alain Bretto, �Random Walks In Directed Hypergraphs and Application to Semi-Supervised Image Segmentation,� Computer Vision and Image Understanding, Volume 120, pp. 91�102, 2013.
[34] Vasileios Zografos, Reiner Lenz, Michael Felsberg, �The Weibull Manifold in Low-Level Image Processing: An Application to Automatic Image Focusing,�, Image and Vision Computing, Volume 31, pp. 401�417, 2013.
[35] Weibao Zou, �Improvement of Panchromatic IKONOS Image Classification Based on Structural Neural Network�, in Lecture Notes in Computer Science Volume 7951, pp. 411�420, 2013.
[36] Q. Xu, et al., �A Novel Approach for Enhancing Very Dark Image Sequences�, Signal Processing, Article in Press, 2014.
[37] Andrews Sobral , Antoine Vacavant, �A Comprehensive Review of Background Subtraction Algorithms Evaluated with Synthetic And Real Videos,� in Computer Vision and Image Understanding, Volume 122, pp. 4�21,2014.
[38] Thomas Schr�der, Klaus Kr�ger, Felix K�mmerlen, �Image Processing based Deflagration Detection using Fuzzy Logic Classification�, Fire Safety Journal Volume 65, pp.1�10, 2014.