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Nafeesa Begum, J.
- The Enhancement of Image Quality in Visual Image Processing by Using Pixel based Digital Filters
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Authors
Affiliations
1 Department of Computer Science and Engineering, Government College of Engineering, Bargur, IN
2 Department of Electronics and Communication Engineering, G Pulla Reddy Engineering College, IN
3 Department of Computer Science and Engineering, D Y Patil Agricultural and Technical University, IN
4 Department of Computer Science and Engineering, D Y Patil College of Engineering and Technology, IN
1 Department of Computer Science and Engineering, Government College of Engineering, Bargur, IN
2 Department of Electronics and Communication Engineering, G Pulla Reddy Engineering College, IN
3 Department of Computer Science and Engineering, D Y Patil Agricultural and Technical University, IN
4 Department of Computer Science and Engineering, D Y Patil College of Engineering and Technology, IN
Source
ICTACT Journal on Image and Video Processing, Vol 13, No 1 (2022), Pagination: 2781-2785Abstract
Digital image processing makes it possible to use the most sophisticated methods, so it can provide both more complex performance in simple tasks and the implementation of impossible methods. Digital cameras usually have a special hardware for image processing (specially added to the circuits or other chips) to convert the original data into a standard image file format in the form of a standard image file changes. The Digital filters are used to blur and sharpen visual based digital images. These filters can be done in the space sphere through the frequency field by covering specially designed cores or certain frequency bands. The images are usually stacked before the Fourier is transferred and the overlay filter images show the results of different layer techniques. In this paper, an innovation filter model was proposed interms of color pixels in the images. The proposed model has a high-pass filter shows extra edges when zero is compared to the repeat edge. The visual transformations make the basic changes of the film, including scaling, rotation, exchange, reflection and cultivation.Keywords
Digital Image Processing, Digital Camera, Digital Filters, Visual, Frequency Bands, Filter Model, Color Pixel.References
- T. Brox, L. Bourdev and S. Maji, “Object Segmentation by Alignment of Poselet Activations to Image Contours”, Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, pp 2225–2232, 2015.
- G. Dhiman, A.V. Kumar, R. Nirmalan and S. Sujitha, “Multi-Modal Active Learning with Deep Reinforcement Learning for Target Feature Extraction in Multi-Media Image Processing Applications”, Multimedia Tools and Applications, Vol. 89, pp. 1-25, 2022.
- M. Ramkumar, N. Basker, D. Pradeep and R. Prajapati, “Healthcare Biclustering-Based Prediction on Gene Expression Dataset”, BioMed Research International, Vol. 2022, pp. 1-8, 2022.
- S. Hannah, A.J. Deepa, V.S. Chooralil and S. Brilly Sangeetha, “Blockchain-Based Deep Learning to Process IoT Data Acquisition in Cognitive Data”, BioMed Research International, Vol. 2022, pp. 1-9, 2022.
- G. Csurka, L. Fan and C. Bray, “Visual Categorization with Bags of Keypoints”, Proceedings of Workshop on Statistical Learning in Computer Vision, pp. 1-2, 2004.
- CVonline: Image Databases, Available at http://homepages.inf.ed.ac.uk/rbf/CVonline/Imagedbase.htm, Accessed at 2021.
- N. Dalal and B. Triggs, “Histograms of Oriented Gradients for Human Detection”, Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, pp. 886-893, 2005.
- S. Kumar, “Neighborhood Pixels Weights-A New Feature Extractor”, International Journal of Computer Theory and Engineering, Vol. 2, No. 1, pp. 1793-8201, 2010.
- Il-Seok Oh and Ching Y. Suen, “Distance Features for Neural Network-based Recognition of Handwritten Characters”, International Journal on Document Analysis and Recognition, Vol. 1, No. 2, pp. 73-88, 1998.
- S. Kumar, “Recognition of Pre-Segmented Devanagari Handwritten Characters using Multiple Features and Neural Network Classifier”, Ph.D. Thesis, Panjab University, 2008.
- Richard E. Woods and Rafael C. Gonzalez, “Digital Image Processing”, Prentice Hall Professional Technical Reference, 1992.
- Philip D. Wasserman, “Advanced Methods in Neural Computing”, Van Nostrand Reinhold, 1993.
- Vikas J. Dongre and Vijay H. Mankar, “A Review of Research on Devanagari Character Recognition”, International Journal of Computer Applications, Vol. 12, No. 2, pp. 8-15, 2010.
- J. Tou and R. Gonzalez, “Pattern Recognition Principles”, Addison-Wesley, 1974.
- The Berkeley Segmentation Dataset and Benchmark, Available:http://www.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/
- Marco Antonio Garcia Carvalho and André Luis Costa, “Combining hierarchical structures on graphs and normalized cut for image segmentation”, New Frontiers in Graph Theory, pp. 389-406, 2012.
- G. Anbarjafari and H. Demirel, “Image Super Resolution Based on Interpolation of Wavelet Domain High Frequency Sub-bands and the Spatial Domain Input Image”, Electronics and Telecommunications Research Institutes Journal, Vol. 32, No. 3, pp. 390-394, 2010.
- G. Anbarjafari and H. Demirel, “Image Resolution enhancement by using Discrete and Stationary Wavelet Decomposition”, IEEE Transactions on Image Processing, Vol. 20, No. 5, pp. 1458-1460, 2011.
- M.B. Chappalli and N.K. Bose, “Simultaneous Noise Filtering and Super-Resolution with Second-Generation Wavelets”, IEEE Signal Processing Letters, Vol. 12, No. 11, pp.772-775, 2005.
- D. L. Ward, “Redundant Discrete Wavelet transform based Super-Resolution using Sub-Pixel Image Registration”, Storming Media Publisher, 2003.
- A.K. Naik and R.S. Holambe, “Design of Low-Complexity High-Performance Wavelet Filters for Image Analysis”, IEEE Transactions on Image Processing, Vol. 22, No. 5, pp. 1848-1858, 2013.
- P.P. Vaidyanathan, “Multirate Systems and Filter Banks”, Pearson Education, India, 1993.