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Kanagalakshmi, K.
- Frequency Domain Enhancement Filters for Fingerprint Image:A Performance Evaluation
Abstract Views :139 |
PDF Views:1
Authors
Affiliations
1 Department of Computer Science, DJ Academy for Managerial Excellence, Coimbatore, Tamilnadu, IN
2 Department of Computer Science, DJ Academy for Managerial Excellence, Coimbatore, Tamilnadu, IN
1 Department of Computer Science, DJ Academy for Managerial Excellence, Coimbatore, Tamilnadu, IN
2 Department of Computer Science, DJ Academy for Managerial Excellence, Coimbatore, Tamilnadu, IN
Source
Digital Image Processing, Vol 3, No 16 (2011), Pagination: 1043-1046Abstract
Filtering and Image Enhancements are the primary need of the automatic identification and authentication system. This paper aims to review and evaluate the frequency domain enhancement techniques: Ideal Low Pass filtering (ILPF), Butterworth Low Pass Filtering (BLPF), Band Pass Filtering (BPF), and Log-Gabor Filtering. Experimental results show the performance measures based on Peak-Signal to Noise Ratio (PSNR) and Mean Square Error (MSE) and also Standard Deviation between original and enhanced image.Keywords
Band-Pass Filter, Butterworth Filter, Domain, Log-Gabor, Low-Pass.
- An Empirical Analysis of Frequency Domain High Pass Filters on Various Types of Noises
Abstract Views :186 |
PDF Views:4
Authors
Affiliations
1 PG and Research Department of Computer Science, Nehru Arts and Science College, Coimbatore, Tamil Nadu, IN
1 PG and Research Department of Computer Science, Nehru Arts and Science College, Coimbatore, Tamil Nadu, IN
Source
Digital Image Processing, Vol 10, No 1 (2018), Pagination: 12-14Abstract
Enhancing the pictorial information for human interpretation is always been a challenging task in digital image processing. Preprocessing is used to remove the unwanted data in digital images. Frequency domain techniques are applied on Fast Fourier transformations of an image. High pass filters are often used to sharpen the digitized image and improve minutiae details. It is frequently applied on the fingerprint images. One of the main objectives of this work is to find the suitable high pass filter for desired noise type. Quality of the images is assessed with image quality metrics like PSNR, MSE and MAE.Keywords
Ideal, Butterworth, Gaussian, Filter, High Pass, Noise.References
- Makandar, Aziz, and Bhagirathi Halalli. "Image enhancement techniques using highpass and lowpass filters." International Journal of Computer Applications, Volume 109 – No. 14, January 2015.
- Shaikh, Md Shahnawaz, Ankita Choudhry, and Rakhi Wadhwani. "Analysis of Digital Image Filters in Frequency Domain." International Journal of Computer Applications, Volume 140 – No.6, April 2016.
- Zawaideh, Farah H., Qais M. Yousef, and Firas H. Zawaideh. "Comparison between Butterworth and Gaussian High-pass Filters using an Enhanced Method." international journal of computer science and network security, Vol.No.17, issue. 7 P.No: 113-117, July 2017.
- Hwang, Jae Jeong, and Kang Hyeon Rhee. "Gaussian filtering detection based on features of residuals in image forensics." In Computing & Communication Technologies, Research, Innovation, and Vision for the Future (RIVF), 2016 IEEE RIVF International Conference on, pp. 153-157. IEEE, 2016.
- Tholeti, Thulasi, Priyanka Ganesh, and Pallavi Ramanujam. "Frequency domain filtering techniques of halftone images." In Signal Processing and Integrated Networks (SPIN), 2015 2nd International Conference on, pp. 427-430. IEEE, 2015.
- Kaur, Jappreet, Manpreet Kaur, Poonamdeep Kaur, and Manpreet Kaur. "Comparative analysis of image denoising techniques." International journal of Emerging Technology and Advanced engineering 2, no. 6 (2012): 296-298.
- Dewangan, Swati, and Anup Kumar Sharma. "Image Smoothening and Sharpening using Frequency Domain Filtering Technique.", International Journal of Emerging Technologies in Engineering Research (IJETER) Volume 5, Issue 4, pp:169-174, April (2017).
- Umbaugh, S. E. 1998, Computer Vision and Image Processing, Prentice Hall PTR, New Jersey.
- Narinder Kaur, Seema Baghla, Sunil Kumar, ”A Review: Image Enhancement and Its Various Techniques”, International Journal of Advances in Science Engineering and Technology, ISSN: 2321-9009 Volume- 3, Issue-3, July-2015.
- Hasinoff, Samuel W., Frédo Durand, and William T. Freeman. "Noise-optimal capture for high dynamic range photography." In Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, pp. 553-560. IEEE, 2010.
- Chandra, E., and K. Kanagalakshmi. "Frequency Domain Enhancement Filters for Fingerprint Image: A Performance Evaluation." Digital Image Processing 3, no. 16 (2011): 1043-1046.
- Image Noise and Filtering Techniques-A Survey
Abstract Views :172 |
PDF Views:4
Authors
Affiliations
1 PG & Research Department Computer Science, Nehru Arts and Science College, Coimbatore, IN
1 PG & Research Department Computer Science, Nehru Arts and Science College, Coimbatore, IN
Source
Digital Image Processing, Vol 10, No 1 (2018), Pagination: 15-17Abstract
An image is a collection of pixels, which can be acquired from the different types of sources. The heterogeneous image sources have high dense noise, this cause several performance related issues in which the image associated with. So, every application under image processing needs an effective technique to perform noise removal on digital images. Image de-noising is an essential step that should be performed before any image analysis process begins. Image noise reduction involves the manipulation of an image to produce a high quality image. This paper gives a survey on recent techniques of image filters. Finally the merits and demerits of existing techniques are identified from the comprehensive study.References
- . Talebi, Hossein, and Peyman Milanfar. "Global image denoising." IEEE Transactions on Image Processing 23, no. 2 (2014): 755-768.
- . Kaur, Sukhjinder. "Noise types and various removal techniques." International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE) Volume 4 (2015).
- . Vijaykumar, V. R., P. T. Vanathi, and P. Kanagasabapathy. "Fast and efficient algorithm to remove gaussian noise in digital images." IAENG International Journal of Computer Science 37, no. 1 (2010): 300-302.
- . Rahman, Tanzila, Mohammad Reduanul Haque, Liton Jude Rozario, and Mohammad Shorif Uddin. "Gaussian noise reduction in digital images using a modified fuzzy filter." In Computer and Information Technology (ICCIT), 2014 17th International Conference on, pp. 217-222. IEEE, 2014.
- . Roy, Amarjit, Joyeeta Singha, Salam Shuleenda Devi, and Rabul Hussain Laskar. "Impulse noise removal using SVM classification based fuzzy filter from gray scale images." Signal Processing 128 (2016): 262-273.
- . Yang, Jian, Jingfan Fan, Danni Ai, Xuehu Wang, Yongchang Zheng, Songyuan Tang, and Yongtian Wang. "Local statistics and non-local mean filter for speckle noise reduction in medical ultrasound image." Neurocomputing 195 (2016): 88-95.
- . Singh, Prabhishek, and Raj Shree. "A comparative study to Noise models and Image restoration Techniques." International Journal of Computer Applications 149, no. 1 (2016).
- . Thakur, Kirti V., Omkar H. Damodare, and Ashok M. Sapkal. "Poisson Noise Reducing Bilateral Filter." Procedia Computer Science 79 (2016): 861-865.
- . Chinnasamy, Gokilavani, and S. Vanitha. "Implementation and Comparison of Various Filters for the Removal of Fractional Brownian Motion noise in Brain MRI Images." IJTET (International Journal for trends in Engineering & Technology), ISSN (2015): 2349-9303.
- . Okada, Mami, Tomoe Ishikawa, and Yuji Ikegaya. "A Computationally Efficient Filter for Reducing Shot Noise in Low S/N Data." PloS one 11, no. 6 (2016): e0157595