3D Image Side Sharpening Using Fourier Phase Only Synthetis Method
DOI:
https://doi.org/10.54209/infosains.v10i2.34Keywords:
3D Image, Side Sharpening, Fourier Phase Only SynthetisAbstract
In the process of the Fourier Phase Only Synthetis method on two images, the observer must get the impression that the imagery actually changes shape to an intermediate form before it changes to the destination image. These changes must occur in a regular and consistent manner to achieve the image of the goal. This sharpening system is one of the systems that aims for this form change process is widely used in applications in the field of entertainment, computer animation, scientific visualization and education. The sharpening system on the 3-dimensional side of the image aims to identify the pattern of the image. Good image quality if it has good contrast and can describe clear ridges and valleys structures. Based on previous research that the study was conducted improvements with Fourier Phase Only Synthetis where the algorithm used simultaneously estimates all the intrinsic properties of. The quality of image sharpening relates to the clarity of ridge structure on the image side. A good image will have a good contrast and will well depict ridges and valleys, if the fingerprint imagery is of poor quality then it will have less contrast so it will less clearly describe the boundaries of ridges (hills). From the implementation of Fourier Phase Only Synthetis Analysis, using the main parameters ridge orientation image, has been successfully obtained the results of image side improvement well. This image side improvement will greatly help to improve the quality of 3-dimensional image extraction, by specifying constant values to get the bestresults.
Downloads
References
P. Li and H. Xiao, “An Improved Filtering Method for Quantum Color Image in Frequency Domain,” Int. J. Theor. Phys., vol. 57, no. 1, 2018, doi: 10.1007/s10773-017-3561-x.
C. J. Russo and R. Henderson, “Ewald sphere correction using a single side-band image processing algorithm,” Ultramicroscopy, vol. 187, 2018, doi: 10.1016/j.ultramic.2017.11.001.
S. R. Fadilah, M. D. M. Manessa, and R. R. Atmawidjaja, “EKSTRAKSI DATA KEDALAMAN MENGGUNAKAN DATA CITRA LANDSAT-8,” J. ONLINE Mhs. Bid. Tek. Geod., vol. 1, 2018.
E. Junianto and M. Z. Zuhdi, “Penerapan Metode Palette untuk Menentukan Warna Dominan dari Sebuah Gambar Berbasis Android,” J. Inform., vol. 5, no. 1, 2018, doi: 10.31311/ji.v5i1.2740.
A. Gandhamal, S. Talbar, S. Gajre, A. F. M. Hani, and D. Kumar, “Local gray level S-curve transformation – A generalized contrast enhancement technique for medical images,” Comput. Biol. Med., vol. 83, 2017, doi: 10.1016/j.compbiomed.2017.03.001.
H. Di and D. Gao, “Gray-level transformation and Canny edge detection for 3D seismic discontinuity enhancement,” Comput. Geosci., vol. 72, 2014, doi: 10.1016/j.cageo.2014.07.011.
J. J. De Santiago-Perez, J. R. Rivera-Guillen, J. P. Amezquita-Sanchez, M. Valtierra-Rodriguez, R. J. Romero-Troncoso, and A. Dominguez-Gonzalez, “Fourier transform and image processing for automatic detection of broken rotor bars in induction motors,” Meas. Sci. Technol., vol. 29, no. 9, 2018, doi: 10.1088/1361-6501/aad3aa.
N. Collings, Fourier Optics in Image Processing. 2018.
S. A. Broughton and K. Bryan, Discrete fourier analysis and wavelets: Applications to signal and image processing: Second edition. 2018.
S. Hu et al., “Weakly supervised deep learning for covid-19 infection detection and classification from ct images,” IEEE Access, vol. 8, pp. 118869–118883, 2020.
D. Pandurangan, R. S. Kumar, L. Gebremariam, L. Arulmurugan, and S. Tamilselvan, “Combined Gray Level Transformation Technique for Low Light Color Image Enhancement,” J. Comput. Theor. Nanosci., vol. 18, no. 4, 2021, doi: 10.1166/jctn.2021.9392.