A Comprehensive & Investigative Review of Literature on Digital Image Processing Technique for Multidisciplinary Industrial Application
DOI:
https://doi.org/10.26438/ijcse/v7i10.149155Keywords:
Digital Forensics, Digital Image Processing, Image Manipulation, Contrast Enhancement Edge Detection, SegmentationAbstract
In the era of digital communication, digital image play a important role in most of industrial and corporate forensic applications. Digital imaging has experienced unremarkable revolution in recent decades, and digital images have been used in a increasing number of applications. Digital Images are used as authenticated proof for any crime and if these images do not remain veritable then it will create question on the validation process. Detecting these types of faking has become serious issue. To determine whether a digital image is original or fake is a big challenge. The detection of image meddling in a digital image is a challenging task. This paper presents a literature survey on some of the image influence detection techniques such as image pre-processing, image compression, edge detection, segmentation, contrast enhancement detection, splicing and composition detection, image tampering and more. Comparison of all the techniques finds the better approach for its future research.
References
[1] "Pattern analysis with two-dimensional spectral localization: Applications of two-dimensional S transforms" by L. Mansinha R. G. Stockwell , R. P. Lowe in Physica A vol. 239 pp. 286-295 IEEE-2017
[2] "Space-local spectral texture map based on MR images of MS patients" by H. Zhu, Mayer, Mansinha L. A, Law C. J. Archibald, Luanne J. R in Mitchell MS: Clin. Lab. Res. IEEE-2018
[3] "Removal of phase artifacts from fMRI data using a stockwell transform filter improves brain activity detection" by B. G. Goodyear H. Zhu R. A., Brown J. R. Mitchell, Magn. Reson in vol. 51 pp. 16-21, IEEE-2012
[4] "A new local multiscale Fourier analysis for medical imaging" by H. Zhu B. G., Goodyear R. A., Brown G. Mayer A. G., Law L. Mansinha J. R. in Mitchell Med. Phys. vol. 30 pp. 1134-1141, IEEE-2009
[5] “Image manipulation detection” by S. Bayram, I. Avcubas, B. Sankur, and N. Memon, J. Electron. Imag., vol. 15, no. 4, pp. 04110201–04110217, 2006.
[6] “Digital image forensics via intrinsic fingerprints” by A. Swaminathan, M. Wu, and K. J. R. Liu, IEEE Trans. Inf. Forensics Security, vol. 3, no. 1, pp. 101–117, Mar. 2008.
[7] “Manipulation detection on image patches using FusionBoost” by H. Cao and A. C. Kot, IEEE Trans. Inf. Forensics Security, vol. 7, no. 3, pp. 992–1002, Jun. 2012.
[8] “Estimating EXIF parameters based on noise features for image manipulation detection” by J. Fan, H. Cao, and A. C. Kot, IEEE Trans. Inf. Forensics Security, vol. 8, no. 4, pp. 608–618, Apr. 2013.
[9] “Forensic detection of image manipulation using statistical intrinsic fingerprints”, M. C. Stamm and K. J. R. Liu, IEEE Trans. Inf. Forensics Security, vol. 5, no. 3, pp. 492–506, Sep. 2010.
[10] “Forensic estimation and reconstruction of a contrast enhancement mapping”by M. C. Stamm and K. J. R. Liu, in Proc. IEEE Int. Conf. Acoust., Speech Signal, Dallas, TX, USA, Mar. 2010, pp. 1698–1701.
[11] “Reverse engineering of double compressed images in the presence of contrast enhancement” by P. Ferrara, T. Bianchiy, A. De Rosaz, and A. Piva, in Proc. IEEE Workshop Multimedia Signal Process., Pula, Croatia,Sep./Oct. 2013, pp. 141–146.
[12] “Contrast Enhancement-Based Forensics in Digital Images” by Gang Cao, Yao Zhao, Rongrong Ni IEEE transactions on information forensics and security, vol. 9, no. 3, march 2014
[13] Hao Yang, Zu-shu Li, Fang-zheng Xue, Gang Luo, Zao-sheng Zhong, "Human-simulated intelligent technique for of image processing", 2009 Chinese Control and Decision Conference, : 2009. pp: 268 - 273
[14] Valery D. Yurkevich, Nikita A. Stepanov, "Modulation based detection of cornea in image segmentation", International Congress on Ultra Modern Telecommunications and Control, Systems and Workshops (ICUMT), 2014, pp: 434 - 440
[15] Xumei Lin, Yunfei Liu, Yulu Wang, "Design and Research of blurring intensity in image ", Chinese Automation Congress (CAC), 2018, pp: 3701 – 3705
[16] "Distributed vector Processing of a new local MultiScale Fourier transform for medical imaging applications", by Brown, Hongmei, IEEE Transactions on Medical Imaging 2005, Volume: 24, Issue: 5, pp: 689 - 691
[17] "Registering Preprocedure Volumetric Images With Intraprocedure 3-D Ultrasound Using an Ultrasound Imaging Model", A. P. King, K. S. Rhode, Y. Ma, C. Yao, C. Jansenm R. IEEE on Medical Imaging, 2010 , Volume: 29 , Issue: 3, pp: 924 - 937
[18] "High Dynamic Range Image Display With Halo and Clipping Prevention", Gabriele Guarnieri ; Stefano Marsi ; Giovanni Ramponi, IEEE Transactions on Image Processing Year: 2011, Volume: 20, Issue: 5, pp: 1351 - 1362
[19] "Multi-Scale Patch-Based Image Restoration", Vardan Papyan & Michael Elad- Haifa, Israel, IEEE Transactions on Image Processing, 2016, Volume:25, Issue: 1, pp:249-261, IEEE Journals & Magazines.
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