Dr. Srimanta Mandal received his Ph.D. from IIT Mandi, India, in 2017. He has been a postdoctoral fellow with the Department of Electrical Engineering, IIT Madras, India, from 2017 to 2018. Since October 2018, he has been with DA-IICT, Gandhinagar, where he is currently an associate professor. During his PhD, he received a travel grant from IIT Mandi for presenting work at the International Conference on Image Processing 2014, Paris, France. So far, he supervised 20 master’s students in their dissertation/project work and co-supervised 1 PhD student. He has published several articles in national/international journals and conferences. He received the best paper award (runner-up) at the Indian Conference on Computer Vision, Graphics and Image Processing 2018. He served as a reviewer for various conferences and journals. He is a senior member of IEEE and served as an executive committee member of the IEEE SPS Gujarat chapter from 2019 to 2022. He served as an Advisory Group Member, Department of Technical Education, Gujarat. He is a life member of IUPRAI and ISRS. His research interests include image processing, computer vision, and machine learning.

Image Processing, Computer Vision, Machine Learning

Journals:

• N. Chaudhari, S. K. Mitra, S. Mandal, S. Chirakkal, D. Putrevu, and A. Misra, "Edge-Preserving Classification of Polarimetric SAR Images using Wishart Distribution and Conditional Random Field,"  International Journal of Remote Sensing, vol. 43, no.6, pp.2134-2155, 2022.
• N. Chaudhari, S. K. Mitra, S. Chirakkal, S. Mandal, D. Putrevu, and A. Misra, "Discrimination of multi-crop scenarios with polarimetric SAR data using Wishart mixture model, " Journal of Applied Remote Sensing, vol. 15, no.3, pp. 1-21, 2021
• P. Mhasakar, P. Trivedi, S. Mandal, and S. K. Mitra, "Handwritten Digit Recognition Using Bayesian ResNet, " SN Computer Science, vol. 2, Article No. 399, pp. 1-10, 2021.
• S. Mandal, and A. N. Rajagopalan, “Local Proximity for Enhanced Visibility in Haze,” in IEEE Transactions on Image Processing, vol. 29, pp. 2478-2491, 2020.
• K. Purohit, S. Mandal, and A. N. Rajagopalan, “Mixed-dense connection networks for image and video super-resolution,” in Neurocomputing, vol. 398, pp. 360-376, 2020.
• K. Purohit, S. Mandal, and A. N. Rajagopalan, “Multi-level Weighted Enhancement for Underwater Image Dehazing,” Journal of the Optical Society of America A, vol. 36, no. 6, pp. 1098-1108, Jun. 2019.
• S. Mandal, A. Bhavsar and A. K. Sao, “Noise Adaptive Super-Resolution from Single Image via Non-Local Mean and Sparse Representation,” Signal Processing, vol. 132, pp. 134-149, Mar. 2017.
• S. Mandal, A. Bhavsar and A. K. Sao, “Depth Map Restoration from Under-sampled Data,” IEEE Transactions on Image Processing, vol. 26, no. 1, pp. 119-134, Jan. 2017.
• S. Mandal and A. K. Sao, “Employing structural and statistical information to learn dictionary(s) for single image super-resolution in sparse domain,” Signal Processing: Image Communication, vol. 48, pp. 63-80, Oct. 2016.
• S. Mandal, S. Thavalengal, and A. K. Sao, “Explicit and implicit employment of edge related information in super-resolving distant faces for recognition,” Pattern Analysis and Applications, vol. 19, no. 3, pp. 867-884, Aug. 2016.

Conferences/Chapters:

• P. Mhasakar, S. Mandal, and S. K. Mitra, “Multi-stream CNN For Face Anti-SpoofingUsing Color Space Analysis,” Accepted in IAPR International Conference on Computer Vision and Image Processing (CVIP), Dec. 2020, pp. 1-11.
• B. Shah, K. Bhatt, S. Mandal, and S. K. Mitra, “EMOTIONCAPS – Facial Emotion Recognition Using Capsules,” Accepted in International Conference on Neural Information Processing (ICONIP), Nov. 2020, pp.1-8.
• S. Mandal, K. Purohit, and A. N. Rajagopalan, “Color Image Super Resolution in Real Noise,” in 11th Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP 2018), December 18-22, 2018, Hyderabad, India, A. M. Namboodiri, V. Balasubramanian, A. Roy-Chowdhury, and G. Gerig (Eds.). ACM, New York, NY, USA, Article 61, pp.1-9.
• K. Purohit, S. Mandal, and A. N. Rajagopalan, “Scale-recurrent multi-residual dense network for image super resolution,” In: Leal-Taixé L., Roth S. (eds) Computer Vision ECCV 2018 Workshops. ECCV 2018. Lecture Notes in Computer Science, vol 11133. Springer, Cham, pp. 132-149.
• S. Mandal, and A. N. Rajagopalan, “Single Noisy Image Super Resolution by Minimizing Nuclear Norm in Virtual Sparse Domain,” in Rameshan R., Arora C., Dutta Roy S. (eds) Computer Vision, Pattern Recognition, Image Processing, and Graphics. NCVPRIPG 2017. Communications in Computer and Information Science, vol 841. Springer, Singapore, 2018, pp.163-176.
• S. Kumari, S. Mandal, and A. Bhavsar, “Patch Similarity in Transform Domain for Intensity/Range Image Denoising with Edge Preservation,” in Rameshan R., Arora C., Dutta RoyS. (eds) Computer Vision, Pattern Recognition, Image Processing, and Graphics. NCVPRIPG 2017. Communications in Computer and Information Science, vol 841. Springer, Singapore, 2018, pp.257-268.
• P. Kaur, S. Mandal, and A. K. Sao, “Significance of Magnetic Resonance Image Details in Sparse Representation Based Super Resolution,” In: Valdés Hernández M., González-Castro V. (eds) Medical Image Understanding and Analysis. MIUA 2017. Communications in Computer and Information Science, vol 723. Springer, Cham, 2017, pp. 605-615.
• S. Mandal, S. Kumari, A. Bhavsar, and A. K. Sao, “Multi-Scale Image Denoising While Preserving Edges in Sparse Domain,” in Proceedings of the European Workshop on Visual Information Processing (EUVIP), Oct. 2016, pp. 1-6.
• S. Mandal, A. Bhavsar, and A. K. Sao, “Super-resolving a single intensity/range image via non-local means and sparse representation,” in Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP), Dec. 2014, pp. 1-8.
• S. Mandal, A. Bhavsar, and A. K. Sao, “Hierarchical example-based range-image superresolution with edge-preservation,” in IEEE International Conference on Image Processing (ICIP), Oct. 2014, pp. 3867-3871.
• S. Thavalengal., S. Mandal, and A. K. Sao, “Significance of Dictionary for Sparse Coding Based Pose Invariant Face Recognition,” in Proceedings of the Twentieth National Conference on Communications (NCC), Feb. 2014, pp. 1-5.
• S. Mandal and A. K. Sao, “Image De-blurring in Super Resolution Framework,” in Proceedings of the National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), Dec. 2013, pp. 1-4.
• S. Mandal and A. K. Sao, “Edge preserving single image super resolution in sparse environment,” in 20th IEEE International Conference on Image Processing (ICIP), Sept. 2013, pp. 967-971.

• Advanced Image Processing
• Basic Electronic Circuits
• Detection and Estimation Theory
• Digital Image Processing
• Machine Learning
• Pattern Recognition & Machine Learning
• Probability Statistics and Information Theory (Tutor)
• Signals and Systems