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·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667
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The Journal of Information and Optimization Sciences (JIOS) is a world leading journal publishing high quality, rigorously peer-reviewed original research in all mathematically-oriented theoretical and applied topics in information sciences, optimization sciences and related areas since 1980. Subjects include but are not limited to:
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Mathematical computing techniques for enhancing image processing in complex systems
*V. Dankan GowdaCorresponding authordankan.v@bmsit.inDepartment of Electronics and Communication Engineering BMS Institute of Technology and ManagementBangalore, Karnataka, 560119, IndiaView full profile →
, B. C. Kavithakavithabc@bgsit.ac.inDepartment of Electronics and Communication Engineering BGS Institute of Technology (BGSIT) Adichunchanagiri UniversityB G Nagara, Karnataka, 571448, IndiaView full profile →
, Madan Mohanrao Jagtapmadan.jagtap@siom.inDepartment of Operations Management Symbiosis Institute of Operations Management Nashik Campus Constituent of Symbiosis International (Deemed University)Pune, Maharashtra, 422008, IndiaView full profile →
, P. Ramesh Naiduramesh.naidu@nmit.ac.inDepartment of Computer Science and Engineering Nitte Meenakshi Institute of Technology Nitte (Deemed to be University)Bangalore, Karnataka, 560064, IndiaView full profile →
, K.D.V. Prasadkdv.prasad@sibmhyd.edu.inDepartment of Research Symbiosis Institute of Business Management; Department of Research Symbiosis International (Deemed University)Symbiosis Institute of Business Management Hyderabad Symbiosis International (Deemed University) Hyderabad, Telangana, 509217, IndiaView full profile →
, Sampathirao Suneethasampath.suneetha@gmail.comDepartment of Computer Science and Engineering Koneru Lakshmaiah Education Foundation (Deemed to be University) VaddeswaramGuntur, Andhra Pradesh, 522502, IndiaView full profile →
* Corresponding author · click or hover a name for details
This paper explores mathematical computing methods of enhancing image processing in complicated systems through noise robustness, outline sharpness and processing speed. Conventional techniques have a problem of noise and computational burden in a number of applications, which include medical imaging and autonomous systems. The study also presents a proposed method that integrates wavelet based denoising, adaptive filtering and optimum feature selection. These performances include a 20% point improvement of the noise reduction’s accuracy over standard approaches due to wavelet optimization and a 30 percentage point decrease of the noise reduction time through the same optimization technique. Therefore, feature extraction methods such as using PCA and DCT had higher accuracy rates in the reduction of the data dimensions provided maintaining core image features. These enhancements support that the framework is even applicable for complex environment solving the essential processing issues.
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