A Technical Analysis of Digital Image and Video Processing

AbstractVideo processing is the most emergent area of image processing in Research and Development. This paper, we present some advanced technology interrelated to video and image processing, which will provide a super-resolution high-quality most prominent visible light video for better human visualization and background motions or gesticulations of the human brain. There are two approaches for increasing the current resolution level of an image. The first one is the improvement of spatial resolution by reducing the size of pixels through the techniques of sensor manufacturing by about 40 Mm2 for a 0.35 Mm CMOS process and the second approach is the enhancement of chip size that also enhances the capacitance. In video processing, we mainly focus on object images, i.e. any form of signal processing in which the input is an image and the output is a photograph or a frame of video. So, it will be better to converge over techniques of digital image processing. In the proposed algorithm (MHI Simulink model), edge extraction is performed to find the precious area of MRI and CT-Scan images. Using FPGA, edge detection methods have been implemented over scanned images to get better accuracy. The accuracy of the proposed method has been compared with Prewitt and Sobel’s edge detection techniques. The proposed method has given better accuracy than Prewitt and Sobel methods. Finally, this paper shows future directions for researchers to enhance the characteristics of digital image and video processing.KEYWORDS: CMOSFPGAgesticulationsimage processingmultimediasignal processingsuper resolutionvideo processing Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work has been supported by Microsoft Research [grant #00138000679].Notes on contributorsPrasanta Kumar SahooPrasanta Kumar Sahoo received a bachelor’s degree in computer science and a master’s degree in computer science and engineering from Utkal University, Odisha, India in 2004 and 2014, respectively. He is pursuing a PhD in information technology and engineering at Odisha University of Technology and Research (O. U. T. R.), Odisha, India. His current research interest includes artificial intelligence, image processing, machine learning, cellular automata, and algorithm designing. He has authored 2 internationally referred journals, 1 conference proceedings, and prepared many research articles in the aforementioned areas. Email: mrprasantasahoo@gmail.comDebasis GountiaDebasis Gountia received the Mtech degree in computer science and engineering (CSE) from the Indian Institute of Technology (IIT) Kharagpur, India. He received the Btech degree in CSE from the UCE Burla, India. He received a PhD award in CSE from the IIT Roorkee, India after being recommended and highly praised by Ritsumeikan University Japan and North Texas University USA. He has over 20 years of teaching and research experience in various organizations. His research interests include electronic design automation of microfluidic lab-on-a-chips, cryptography, and artificial intelligence. He has authored 16 internationally referred journals, 15 conference proceedings, 3 books, 4 book chapters for CRC Press, IEEE/ACM Transaction, and two filed patents in the aforementioned areas. He has been carrying out many international projects including two AI for Earth Azure Compute Grants of Microsoft Pvt. Ltd., USA as a team leader and submitted a project proposal on COVID-19 Control using the AI-Powered Mass Corona Surveillance System for Microsoft AI for Health COVID-19 Grant, Geospatial Project of ISRO, ICIT, DETECT-X for Innovate4Health design sprint, etc. He has been a reviewer of various journals and transactions, along with conferences and reviewed many articles for IEEE/ACM Transactions on Computational Biology and Bioinformatics, IEEE Access, Integration the VLSI Journal, ACM Journal on Emerging Technologies in Computing Systems, and several IEEE conferences. He is a professional member of ACM, IEEE, SMIE, FSIESRP, and IFERP along with many international and national awards. Corresponding author. Email: dgountia@cs.iitr.ac.inRanjan Kumar DashRanjan Kumar Dash received his PhD degree from Sambalpur University, India in 2008. Currently, he is working as a professor and head of the department of information technology, Odisha University of Technology and Research, Bhubaneswar, India. He has served on the technical program and organization committees of several conferences. His primary research interests include the reliability of distributed systems, wireless sensor networks, soft computing, machine learning, and cloud computing. His current research focus is on enhancing processor performance and energy consumption through the use of machine-learning techniques. He has more than 42 publications in different international journals/conferences. Email: rkdash@outr.ac.inJahangir MohammedJahangir Mohammed received his MSc and MPhil degrees in physics from Utkal University, Odisha, India in 2001 and 2004, respectively. He obtained his Mtech degree in computer science from the Indian Statistical Institute (ISI), Kolkata, West Bengal, India, in 2009. He is pursuing a PhD in physics at Utkal University, Odisha, India. He is currently working as a lecturer in the department of physics at Nabarangpur Degree College, Odisha, India. His current research interests include cellular automata, pattern recognition, image processing, statistical mechanics, and computational physics. Email: jahangirmd.physics@gmail.com