Krishna Raj

Work place: Dept. of Electronics Engineering, Harcourt Butler Technical University, Kanpur, 208002, India



Research Interests: Image Processing, Image Manipulation, Image Compression, Computer systems and computational processes


Dr. Krishna Raj did his Bachelor’s degree in Electronics Engineering from M.M.M.E.C., Gorakhpur in 1992 and pursued his Master’s degree in Electronics Engineering with Control & Instrumentation as specialization from M.N.N.I.T., Allahabad (formerly M.N.R.E.C.), Uttar Pradesh, India in 1995. He has completed his doctoral degree in Electronics Engineering from M.M.M.E.C. Gorakhpur; D.D.U. University of Gorakhpur, Uttar Pradesh State, in 2003. His field of interests includes digital signal processing, image processing, computer arithmetic, and VLSI. Currently, he is working as a professor in Electronic Engineering department of Harcourt Butler Technical University, Kanpur, Uttar Pradesh state. He has over 22 years of teaching and research experience. He has published more than 42 technical papers in national and international conferences and more than 20 articles/ research papers in journals. He has authored 3 books in the field of signal processing and digital logic design. Dr. K. Raj has the fellowship of IETE and membership of IEEE. He is also a life member of IE.

Author Articles
Edge Detection based on Ant Colony Optimization Using Adaptive Thresholding Technique

By Pragya Gautam Krishna Raj

DOI:, Pub. Date: 8 Jul. 2018

Image edge detection is a process where true edges of an image are identified. In past, gradient based methods in which first or second order pixel difference is used to find discontinuities and if magnitude value of gradient is higher than certain threshold then that pixel under observation is identified as edge pixel. These methods are full of error, because in addition to true edges they also find false edges and infect false edges are more in comparison to true edges. To solve such problem, swarm intelligence based ant colony optimization based edge detection method is detailed where numbers of falsely detected edges are very small. The performance of the ant colony optimization (ACO) is done in terms of Peak Signal to Noise Ratio, Performance Ratio and Efficiency.

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Removal of Image Blurring and Mix Noises Using Gaussian Mixture and Variation Models

By Vipul Goel Krishna Raj

DOI:, Pub. Date: 8 Jan. 2018

For the past recent decades, image denoising has been analyzed in many fields such as computer vision, statistical signal and image processing. It facilitates an appropriate base for the analysis of natural image models and signal separation algorithms. Moreover, it also turns into an essential part to the digital image acquiring systems to improve qualities of an image. These two directions are vital and will be examined in this work. Noise and Blurring of images are two degrading factors and when an image is corrupted with both blurring and mixed noises, de-noising and de-blurring of the image is very difficult. In this paper, Gauss-Total Variation model (G-TV model) and Gaussian Mixture-Total Variation Model (GM-TV Model) are discussed and results are presented. It is shown that blurring of the image is completely removed using G-TV model; however, image corrupted with blurring and mixed noise can be recovered with GM-TV model.

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