ORCID ID: https://orcid.org/0000-0002-6738-7786

SCOPUS Author ID: 37862221600

 Google Scholar Citations (Feb 2020)

 Citations: 197;      h-index: 6        i10-index: 5

Number of Publications (Feb 2020-Present)

#National Journals/Conference: 5;      #International Conference: 5        #International Journals: 8

International Journal (2013 - Present):

1. Sudeep, P.V., Palanisamy, P., Kesavadas, C. and Rajan, J., 2015. Nonlocal linear minimum mean square error methods for denoising MRI. Biomedical Signal Processing and Control, 20, pp.125-134.
2. Sudeep, P.V., Niwas, S.I., Palanisamy, P., Rajan, J., Xiaojun, Y., Wang, X., Luo, Y. and Liu, L., 2016. Enhancement and bias removal of optical coherence tomography images: an iterative approach with adaptive bilateral filtering. Computers in biology and medicine, 71, pp.97-107.
3. Sudeep, P.V., Palanisamy, P., Rajan, J., Baradaran, H., Saba, L., Gupta, A. and Suri, J.S., 2016. Speckle reduction in medical ultrasound images using an unbiased non-local means method. Biomedical Signal Processing and Control, 28, pp.1-8.
4. Sudeep, P.V., Palanisamy, P., Kesavadas, C., Sijbers, J., Arnold, J. and Rajan, J., 2017. A nonlocal maximum likelihood estimation method for enhancing magnetic resonance phase maps. Signal, Image and Video Processing, 11(5), pp.913-920.
5. Girish, G.N., Anima, V.A., Kothari, A.R., Sudeep, P.V., Roychowdhury, S. and Rajan, J., 2018. A benchmark study of automated intra-retinal cyst segmentation algorithms using optical coherence tomography B-scans. Computer methods and programs in biomedicine, 153, pp.105-114.
6. Yamanappa, W., Sudeep, P.V., Sabu, M.K. and Rajan, J., 2018. Non-local means image denoising using Shapiro-Wilk similarity measure. IEEE Access, 6, pp.66914-66922.
7.  Sudeep, P.V., Palanisamy, P., Kesavadas, C. and Rajan, J., 2020. An improved nonlocal maximum likelihood estimation method for denoising magnetic resonance images with spatially varying noise levels. Pattern Recognition Letters,139,pp.34-41.
8. Naveen, S., Ram Kiran, M.S.S, Indupriya, M., Manikanta, T.V., Sudeep, P.V., 2021. A Transformer models for enhancing AttnGAN based text to image generation. Image and Vision Computing,115,pp.104284.
9. Krishnan, N., Shone, S.J., Sashank, C.S., Ajay, T.S., Sudeep, P.V., 2022. A hybrid low-light image enhancement method using Retinex decomposition and deep light curve estimation. Optik,260, pp.169022.
10. Revati Suresh, K., Arun Jarapala, Sudeep, P.V., 2022. Image captioning encoder-decoder models using CNN-RNN architectures: A comparitive study. Circuits, Systems & Signal Processing,xx,pp.xxxx (Accepted)
11. Anate Augustin, Kesavadas, C., Sudeep, P.V., 2022. An improved deep persistent memory network for Rician noise reduction in MR images. Biomedical Signal Processing & Control,xx,pp.xxxx (Accepted)

International Conference :

1. Sudeep, P.V., Navas, K.A. and Sheeba, V.S., 2009, December. Blind Datahiding in Integer Wavelet Transform Domain. In International Conference on Modeling and Simulation (MS09) India (Vol. 1, p. 3).
2. Sudeep, P.V., Palanisamy, P. and Rajan, J., 2013, December. A hybrid model for Rician noise reduction in MRI. In 2013 2nd International Conference on Advanced Computing, Networking and Security (pp. 56-61). IEEE.
3. Yamanappa, W, Sudeep, P.V. and Rajan, J., 2019, March. An improved multi-stage multiple image based non-local means image denoising. In International Conference on Machine Learning, Image Processing, Network Security and Data Sciences (MIND 19).
4. Harshavardhan, G, Sri Venkat, G., Pratyusha, M.G., Kunal Gupta, Volam Priyanka and Sudeep, P.V., 2019, May.  Hardware Implementation of Sign Language to Text Converter using Deep Neural Networks. In International Conference on “Advances in Electronics, Electrical & Computational Intelligence” (ICAEEC 19).
5. Vineeth Murali and Sudeep, P.V., 2019, December.  Image Denoising using DnCNN: An Exploration Study. In Springer International Conference on “Communication Systems and Networking” (ComNet 2019).

1. Sudeep, P.V., Palanisamy, P. and Rajan, J., 2019. Advances in Ultrasound Despeckling: An Overview. In Advanced Classification Techniques for Healthcare Analysis (pp. 311-335). IGI Global.
2. Anoop, B.N., Girish, G.N., Sudeep, P.V. and Rajan, J., 2019. Despeckling Algorithms for Optical Coherence Tomography Images: A Review. In Advanced Classification Techniques for Healthcare Analysis (pp. 286-310). IGI Global.

UG Courses

1. Digital Communication
2. Communication Theory and Systems I
3. Communication Theory and Systems II
4. Communication Labs

PG Courses

1. Neural Networks
2. Digital Image Processing Techniques

• Life member, The Indian Society for Technical Education (ISTE)
• Member, Institute of Electrical and Electronics Engineers (IEEE)
• Member, Indian Society of Electronics and Communication Engineers (ISECE)

1. Title: Development of an indigenous tool for the enhancement of medical images using deep neural networks|Agency: Science and Engineering Research Board (SERB)|Scheme: Start-up Research Grant|Duration: Two years (2019-2022)|Total cost: Rs.30 Lakhs (Ongoing)

PhD (Ongoing) - 2

1. Nabeel Koya A (Part-time)
2. Hari Kishan R (Full-time)

M.Tech (Ongoing) - 2

1. Vineeth Murali
2. Revathi Suresh