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Current Chinese Computer Science

Editor-in-Chief

ISSN (Print): 2665-9972
ISSN (Online): 2665-9964

Research Article

Image Enhancement with Improved Global and Local Visual Effects

Author(s): Muhammad Adeel and Yinglei Song*

Volume 1, Issue 2, 2021

Published on: 02 February, 2021

Article ID: e030621191046 Pages: 12

DOI: 10.2174/2665997201666210203094041

Abstract

Background: In many applications of image processing, the enhancement of images is often a step necessary for their preprocessing. In general, for an enhanced image, the visual contrast as a whole and its refined local details are both crucial for achieving accurate results for subsequent classification or analysis.

Objective: This paper proposes a new approach for image enhancement such that the global and local visual effects of an enhanced image can both be significantly improved.

Methods: The approach utilizes the normalized incomplete Beta transform to map pixel intensities from an original image to its enhanced one. An objective function that consists of two parts is optimized to determine the parameters in the transform. One part of the objective function reflects the global visual effects in the enhanced image and the other one evaluates the enhanced visual effects on the most important local details in the original image. The optimization of the objective function is performed with an optimization technique based on the particle swarm optimization method.

Results: Experimental results show that the approach is suitable for the automatic enhancement of images.

Conclusion: The proposed approach can significantly improve both the global and visual contrasts of the image.

Keywords: Image enhancement, global visual effects, local visual effects, normalized incomplete beta transform, optimization, particle swarm optimization.

Graphical Abstract
[1]
T.L. Ji, M.K. Sundareshan, and H. Roehrig, "Adaptive image contrast enhancement based on human visual properties", IEEE Trans. Med. Imaging, vol. 13, no. 4, pp. 573-586, 1994.
[http://dx.doi.org/10.1109/42.363111] [PMID: 18218535]
[2]
J.D. Tubbs, "A note on parametric image enhancement", Pattern Recognit., vol. 20, no. 6, pp. 617-621, 1987.
[http://dx.doi.org/10.1016/0031-3203(87)90031-8]
[3]
S.S. Agaian, K. Panetta, and A.M. Grigoryan, "Transform-based image enhancement algorithms with performance measure", IEEE Trans. Image Process., vol. 10, no. 3, pp. 367-382, 2001.
[http://dx.doi.org/10.1109/83.908502] [PMID: 18249627]
[4]
X. Su, W. Fang, Q. Shen, and X. Hao, "An image enhancement method using the quantum-behaved particle swarm optimization with an adaptive strategy", Math. Probl. Eng., vol. 2013, p. 824787, . 2013
[http://dx.doi.org/10.1155/2013/824787]
[5]
V.L. Jaya, and R. Gopikakumari, "IEM: a new image enhancement metric for contrast and sharpness measurements", Int. J. Comput. Appl. , vol. 79, no. 9, p. 3891620, . 2013
[6]
M. Wan, G. Gu, W. Qian, K. Ren, Q. Chen, and X. Maldague, "Particle swarm optimization-based local entropy weighted histogram equalization for infrared image enhancement", Infrared Phys. Technol., vol. 91, pp. 164-181, 2018.
[http://dx.doi.org/10.1016/j.infrared.2018.04.003]
[7]
M. Gharbi, J. Chen, J.T. Barron, S.W. Hasinoff, and F. Durand, "Deep bilateral learning for real-time image enhancement", ACM Trans. Graph., vol. 36, no. 4, p. 118, 2017.
[http://dx.doi.org/10.1145/3072959.3073592]
[8]
K.G. Lore, A. Akintayo, and S. Sarkar, "LLNet: a deep autoencoder approach to natural low-light image enhancement", Pattern Recognit., vol. 61, pp. 650-662, 2017.
[http://dx.doi.org/10.1016/j.patcog.2016.06.008]
[9]
L. Tao, C. Zhu, J. Song, T. Lu, H. Jia, and X. Xie, "Low-light image enhancement using CNN and bright channel prior", IEEE International Conference on Image Processing, 2017pp. 3215-3229
[http://dx.doi.org/10.1109/ICIP.2017.8296876]
[10]
L. Tao, C. Zhu, G. Xiang, Y. Li, H. Jia, and X. Xie, LLCNN: a convolutional neural network for low-light image enhancement..IEEE visual communications and image processing, pp. 1-4, 2017,
[http://dx.doi.org/10.1109/VCIP.2017.8305143]
[11]
Xiaojie Guo, Yu Li, and Haibin Ling, "LIME: low-light image enhancement via illumination Map Estimation", IEEE Trans. Image Process., vol. 26, no. 2, pp. 982-993, 2017.
[http://dx.doi.org/10.1109/TIP.2016.2639450] [PMID: 28113318]
[12]
G. Li, M.N.A. Rana, J. Sun, Y. Song, and J. Qu, "Real-time image enhancement with efficient dynamic programming", Multimedia Tools Appl., 2020.
[http://dx.doi.org/10.1007/s11042-020-09586-y]
[13]
J. Tang, E. Peli, and S. Acton, "Image enhancement using a contrast measure in the compressed domain", IEEE Signal Process. Lett., vol. 10, no. 10, pp. 289-292, 2003.
[http://dx.doi.org/10.1109/LSP.2003.817178]
[14]
S.D. Chen, and A.R. Ramli, "Minimum mean brightness error bi-histogram equalization in contrast enhancement", IEEE Trans. Consum. Electron., vol. 49, no. 4, pp. 1310-1319, 2003.
[http://dx.doi.org/10.1109/TCE.2003.1261234]
[15]
R.A. Hummel, "Histogram modification techniques", Comput. Graph., vol. 4, no. 3, pp. 209-224, 1975.
[16]
A.B. Watson, Digital Images and Human Vision., The MIT Press: Massachusetts, Mass, USA, 1993.
[17]
A. Toet, "Multiscale color image enhancement", Pattern Recognit. Lett., vol. 13, no. 3, pp. 167-174, 1992.
[http://dx.doi.org/10.1016/0167-8655(92)90056-6]
[18]
P.E. Trahanias, and A.N. Venetsanopoulos, "Color image enhancement through 3-D histogram equalization", Proceedings of the 11th IAPR International Conference on Pattern Recognition, 1992pp. 545-548
[http://dx.doi.org/10.1109/ICPR.1992.202045]
[19]
Q. Li, H. Wu, L. Xu, L. Wang, Y. Lv, and X. Kang, "Low-light image enhancement based on deep symmetric encoder–decoder convolutional Networks", Symmetry (Basel), vol. 12, p. 446, 2020.
[http://dx.doi.org/10.3390/sym12030446]
[20]
M. Li, J. Liu, W. Yang, X. Sun, and Z. Guo, "Structure-revealing low-light image enhancement via robust retinex model", IEEE Trans. Image Process., vol. 27, no. 6, pp. 2828-2841, 2018.
[http://dx.doi.org/10.1109/TIP.2018.2810539] [PMID: 29570085]
[21]
S. Ai, and J. Kwon, "Extreme low-light image enhancement for surveillance cameras using attention u-net", Sensors (Basel), vol. 20, no. 2, p. 495, 2020.
[http://dx.doi.org/10.3390/s20020495] [PMID: 31952325]
[22]
J. Kim, J.K. Lee, and K.M. Lee, "Accurate image super-resolution using very deep convolutional networks", Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016pp. 1646-1654
[http://dx.doi.org/10.1109/CVPR.2016.182]
[23]
J. Kennedy, and R. Eberhart, "Particle swarm optimization", Proceedings of the 1995 IEEE International Conference on Neural Networks 1995, 1944pp. 1942-1948
[24]
R.C. Eberhart, and Y. Shi, "Particle swarm optimization: developments, applications and resources", Proceedings of the IEEE Conference on Evolutionary Computation, 2001pp. 81-86
[http://dx.doi.org/10.1109/CEC.2001.934374]
[25]
P. Arbeláez, M. Maire, C. Fowlkes, and J. Malik, "Contour detection and hierarchical image segmentation", IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 5, pp. 898-916, 2011.
[http://dx.doi.org/10.1109/TPAMI.2010.161] [PMID: 20733228]

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