A Comprehensive Review on Various State-of-the-Art Techniques for Image Enhancement
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https://doi.org/10.14419/ijet.v7i3.34.19576
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Face recognition, image enhancement techniques, contrast and visibility, uneven illumination, Human Visual System -
Abstract
Image processing involves many pre-processing techniques. One such technique is image enhancement. This is the most difficult phase in processing because it should enhance an image to such a clear visual level that human eyes should discern it. It is proven that the enhanced images are able to provide the high rate accuracy, increased efficiency, robust results in case of criminal investigations, security applications etc. Here, the goal is to enhance a degraded image, noisy, foggy or low-resolution image to obtain the output image which appears better than the original. This survey paper provides a brief analysis of techniques and algorithms of image enhancement.
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References
[1] Kapil, D. & Abhilasha (2015, September). Face recognition of blurred images using image enhancement and texture features. In Next Generation Computing Technologies (NGCT), 2015 1st International Conference on (pp. 894-897). IEEE.
[2] Sharma, N., Saurav, S., Singh, S., Saini, R., & Saini, A. K. (2015, August). A comparative analysis of various image enhancement techniques for facial images. In Advances in Computing, Communications and Informatics (ICACCI), 2015 International Conference on (pp. 2279-2284). IEEE.
[3] Selamat, M. H., & Rais, H. M. (2016, August). Enhancement on image face recognition using Hybrid Multiclass SVM (HM-SVM). In Computer and Information Sciences (ICCOINS), 2016 3rd International Conference on (pp. 424-429). IEEE.
[4] Nithyananda, C. R., & Ramachandra, A. C. (2016, March). Review on histogram equalization based image enhancement techniques. In Electrical, Electronics, and Optimization Techniques (ICEEOT), International Conference on (pp. 2512-2517). IEEE.
[5] Yadav, G., Maheshwari, S., & Agarwal, A. (2014, September). Contrast limited adaptive histogram equalization based enhancement for real time video system. In Advances in Computing, Communications and Informatics (ICACCI, 2014 International Conference on (pp. 2392-2397). IEEE.
[6] Vidya, V., Farheen, N., Manikantan, K., & Ramachandran, S. (2012). Face recognition using threshold based DWT feature extraction and selective illumination enhancement technique. Procedia Technology, 6, 334-343.
[7] Amani, N., Shahbahrami, A., & Nahvi, M. (2013). A new approach for face image enhancement and recognition. International Journal of Advanced Science and Technology, 52(01), 1-10.
[8] Lim, C. S., & Ibrahim, H. (2013). Image enhancement for face images using spatial domain processing. International Journal of Advanced Research in Computer Science and Electronics Engineering (IJARCSEE), 2(11), pp-714.
[9] Ibrahim, H., & Kong, N. S. P. (2007). Brightness preserving dynamic histogram equalization for image contrast enhancement. IEEE Transactions on Consumer Electronics, 53(4).
[10] Wan, W., & Lee, H. J. (2017, October). A detail enhancement strategy for face sketch synthesis based on NSST. In Information and Communication Technology Convergence (ICTC), 2017 International Conference on (pp. 784-788). IEEE.
[11] Saleem, A., Beghdadi, A., & Boashash, B. (2012). Image fusion-based contrast enhancement. EURASIP Journal on Image and Video Processing, 2012(1), 10.
[12] Shyu, M. S., & Leou, J. J. (1998). A genetic algorithm approach to color image enhancement. Pattern Recognition, 31(7), 871-880
[13] Sharumathi, K., & Priyadharsini, R. (2016, March). A survey on various image enhancement techniques for underwater acoustic images. In Electrical, Electronics, and Optimization Techniques (ICEEOT), International Conference on (pp. 2930-2933). IEEE.
[14] Polesel, A., Ramponi, G., & Mathews, V. J. (2000). Image enhancement via adaptive unsharp masking. IEEE transactions on image processing, 9(3), 505-510.
[15] Jin, L., Satoh, S. I., & Sakauchi, M. (2004, August). A novel adaptive image enhancement algorithm for face detection. In Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on (Vol. 4, pp. 843-848). IEEE.
[16] Sangeetha, N., & Anusudha, K. (2017, January). Image defogging using enhancement techniques. In Computer, Communication and Signal Processing (ICCCSP), 2017 International Conference on (pp. 1-5). IEEE.
[17] Wan, Q., & Panetta, K. (2016, May). A facial recognition system for matching computerized composite sketches to facial photos using human visual system algorithms. In 2016 IEEE Symposium on(pp. 1-6).
[18] Wan, Q., Panetta, K., & Agaian, S. (2015, September). Autonomous
facial recognition based on the human visual system. In Imaging Systems and Techniques (IST), 2015 IEEE International Conference on (pp. 1-6). IEEE.
[19] Davis, N., Pittaluga, F., & Panetta, K. (2013, April). Facial recognition
using human visual system algorithms for robotic and UAV platforms.
In Technologies for Practical Robot Applications (TePRA), 2013 IEEE.
[20] Wan, Q., Panetta, K., & Agaian, S. (2017, May). Autonomous facial recognition system inspired by human visual system based logarithmical image visualization technique. In Mobile Multimedia/Image Processing, Security, and Applications 2017 (Vol. 10221, p. 1022106). International Society for Optics and Photonics.
[21] Sapna Josephus, C., & Remya, S. (2012). Enhancement Techniques for Local Content Preservation and Contrast Improvement in Images. arXiv preprint arXiv:1203.1823.
[22] Huang, K. Q., Wang, Q., & Wu, Z. Y. (2006). Natural color image enhancement and evaluation algorithm based on human visual system. Computer Vision and Image Understanding, 103(1), 52-63.
[23] Al-amri, S. S., Kalyankar, N. V., & Khamitkar, S. D. (2010). Linear and non-linear contrast enhancement image. International Journal of Computer Science and Network Security, 10(2), 139-143.
[24] Bedi, S. S., & Khandelwal, R. (2013). Various image enhancement techniques-a critical review. International Journal of Advanced Research in Computer and Communication Engineering, 2(3).
[25] Nancy, E., & Kaur, E. S. (2013). Comparative Analysis and Implementation of Image Enhancement Techniques Using MATLAB. International Journal of Computer Science and Mobile Computing, 2(4).
[26] Mishra, P., Sinha, M. K., Shivakumar, H., Krishnamurthy, K. N., Akashdeep, B. N., Khan, S. N., ... & Shewale, M. S. R. (2014). Different Approaches of Image Enhancement. International Journal of Research in Advent Technology, 2(8), 106-109.
[27] Panetta, K., Zhou, Y., Agaian, S., & Jia, H. (2011). Nonlinear unsharp masking for mammogram enhancement. IEEE Transactions on Information Technology in Biomedicine, 15(6), 918-928
[28] Panetta, K., Agaian, S., Zhou, Y., & Wharton, E. J. (2011). Parameterized logarithmic framework for image enhancement. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 41(2), 460-473
[29] Dhariwal, S. (2011). Comparative analysis of various image enhancement techniques. International Journal of Electronics & Communication Technology (IJECT), 2(3), 91-95.
[30] Bhateja, V., Misra, M., & Urooj, S. (2016). Human visual system based unsharp masking for enhancement of mammographic images. Journal of Computational Science.
[31] Walha, R., Drira, F., Lebourgeois, F., Alimi, A. M., & Garcia, C. (2016). Resolution enhancement of textual images: a survey of single image-based methods. IET Image Processing, 10(4), 325-337
[32] Lee, J. S. (1980). Digital image enhancement and noise filtering by use of local statistics. IEEE transactions on pattern analysis and machine intelligence, (2), 165-168.
[33] Suganya, P., Gayathri, S., & Mohanapriya, N. (2013). Survey on Image Enhancement Techniques. International Journal of Computer Applications Technology and Research, 2(5), 623-meta.
[34] Tang, B., Sapiro, G., & Caselles, V. (2001). Color image enhancement via chromaticity diffusion. IEEE Transactions on Image Processing, 10(5), 701-707.
[35] Maini, R., & Aggarwal, H. (2010). A comprehensive review of image enhancement techniques. arXiv preprint arXiv:1003.4053.
[36] Pizer, S. M., Amburn, E. P., Austin, J. D., Cromartie, R., Geselowitz, A., Greer, T., ... & Zuiderveld, K. (1987). Adaptive histogram equalization and its variations. Computer vision, graphics, and image processing, 39(3), 355-368.
[37] Rana, M. E., Zadeh, A. A., & Alqurneh, A. M. M. (2017). Use of image enhancement techniques for improving real time face RECOGNITION EFFICIENCY ON WEARABLE GADGETS. Journal of Engineering Science and Technology, 12(1), 155-167.
[38] Kim, T. K., Paik, J. K., & Kang, B. S. (1998). Contrast enhancement system using spatially adaptive histogram equalization with temporal filtering. IEEE Transactions on Consumer Electronics, 44(1), 82-87.
[39] Lu, L., Zhou, Y., Panetta, K., & Agaian, S. (2010, April). Comparative study of histogram equalization algorithms for image enhancement. In SPIE Defense, Security, and Sensing (pp. 770811-770811). International Society for Optics and Photonics.
[40] Tan, X., & Triggs, B. (2010). Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE transactions on image processing, 19(6), 1635-1650.
[41] Rao, D. H., & Panduranga, P. P. (2006, December). A survey on image enhancement techniques: classical spatial filter, neural network, cellular neural network, and fuzzy filter. In Industrial Technology, 2006. ICIT 2006. IEEE International Conference on (pp. 2821-2826). IEEE.
[42] Rana, M. E., Zadeh, A. A., & Alqurneh, A. M. M. (2017). Use of image enhancement techniques for improving real time face recognition efficiency on wearable gadgets.Journal of Engineering Science and Technology, 12(1), 155-167.
[43] Perumal, K., & Perumal, N. S. (2013). Image enhancement for face recognition using color segmentation and Edge detection algorithm. International Journal of Computer Science & Communication Networks, 3(3), 187.
[44] D'Cunha, N. W., Birajdhar, S. A., Manikantan, K., & Ramachandran, S. (2013, October). Face recognition using Homomorphic Filtering as a pre-processing technique. In Emerging Trends in Communication, Control, Signal Processing & Computing Applications (C2SPCA), 2013 International Conference on (pp. 1-6). IEEE.
[45] Wharton, E., Panetta, K., & Againan, S. (2007, April). Human visual system based multi-histogram equalization for non-uniform illumination and shoadow correction. In Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on (Vol. 1, pp. I-729). IEEE.
[46] Aarthi, R., Anjana, K. P., & Amudha, J. (2016). Sketch based image retrieval using information content of orientation. Indian Journal of Science and Technology, 9(1).
[47] Zhou, Y., Panetta, K., & Agaian, S. (2010, July). Human visual system based mammogram enhancement and analysis. In Image Processing Theory Tools and Applications (IPTA), 2010 2nd International Conference on (pp. 229-234). IEEE.
[48] Kokila, R., Sannidhan, M. S., & Bhandary, A. (2017, March). A study and analysis of various techniques to match sketches to Mugshot photos. In Inventive Communication and Computational Technologies (ICICCT), 2017 International Conference on (pp. 41-44). IEEE.
[49] Sannidhan, M. S., & Ananth Prabhu, G. A Comprehensive Review on Various State-Of-The-Art Techniques for Composite Sketch Matching.
[50] Kokila, R., Sannidhan, M. S., & Bhandary, A. (2017). A novel approach for matching composite sketches to mugshot photos using the fusion of SIFT and SURF feature descriptor.2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI). doi:10.1109/icacci.2017.8126046
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How to Cite
S, P., M S, S., & Bhandary, A. (2018). A Comprehensive Review on Various State-of-the-Art Techniques for Image Enhancement. International Journal of Engineering & Technology, 7(3.34), 860-864. https://doi.org/10.14419/ijet.v7i3.34.19576Received date: 2018-09-12
Accepted date: 2018-09-12