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Faculty

Srimanta Mandal

Srimanta Mandal
PhD (Computing and Electrical Engineering), IIT Mandi
079-68261621 # 4203, FB-4, DA-IICT, Gandhinagar, Gujarat, India – 382007 srimanta_mandal[at]daiict[dot]ac[dot]in https://srimanta-mandal.github.io/

I received the B.Tech degree in Electronics and Communication Engineering from the West Bengal University of Technology, West Bengal, in 2010. After that, I joined IIT Mandi as an MS (by research) Scholar. In 2013, I have been enrolled in the Ph.D. program with a conversion from MS. I received the Ph.D. degree from IIT Mandi in 2017. I have been a postdoctoral fellow with the Department of Electrical Engineering, IIT Madras from 2017 to 2018. Since October 2018, I have been with DA-IICT, Gandhinagar, where I am currently working as an Assistant Professor.

Image Processing, Computer Vision, Machine Learning

Journals:

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