Publications

For a complete and updated list, please refer to my Google Scholar.

Selected Publications

  1. Hong, X., Adam, T., & Ghazali, M. (2025). UHDNet: Unified multimodal fusion harmonization and hierarchical dependency learning for Visible-Infrared Person Re-Identification. Image and Vision Computing, 105628. [DOI]
  2. Adam, T., Paramesran, R., & Ratnavelu, K. (2022). A combined higher order non-convex total variation with overlapping group sparsity for Poisson noise removal. Computational and Applied Mathematics, 41(4), 1-33. [DOI] [Code]
  3. Yin, M., Adam, T., Paramesran, R., & Hassan, M. F. (2022). An ℓ0-overlapping group sparse total variation for impulse noise image restoration. Signal Processing: Image Communication, 102, 116620. [DOI] [Code]

Journal Papers

  1. Hong, X., Adam, T., & Ghazali, M. (2025). Tran‐GCN: A Transformer‐Enhanced Graph Convolutional Network for Person Re‐Identification in Monitoring Videos. IET Computer Vision, 19(1), e70025. [PDF] [DOI]
  2. Yin, M., Adam, T., Paramesran, R., & Fikree Hassan, M. (2025). An ℓ 0 total generalized variation for impulse noise removal. Multimedia Tools and Applications, 84(27), 31813-31839. [DOI] [Code]
  3. Hassan, M. F., Adam, T., Yin, M., & Paramesran, R. (2023). Effect of image denoising on geometric moments in image applications. The Journal of Analysis, 31(3), 1783-1803. [DOI]
  4. Hassan, M. F., Adam, T., Rajagopal, H., & Paramesran, R. (2022). A hue preserving uniform illumination image enhancement via triangle similarity criterion in HSI color space. The Visual Computer, 1-12. [DOI]
  5. Adam, T., Paramesran, R., Mingming, Y., & Ratnavelu, K. (2021). Combined higher order non-convex total variation with overlapping group sparsity for impulse noise removal. Multimedia Tools and Applications, 80(12), 18503-18530. [DOI] [Code]
  6. Adam, T., & Paramesran, R. (2020). Hybrid non-convex second-order total variation with applications to non-blind image deblurring. Signal, Image and Video Processing, 14(1), 115-123. [DOI] [Code]
  7. Adam, T., & Paramesran, R. (2019). Image denoising using combined higher order non-convex total variation with overlapping group sparsity. Multidimensional Systems and Signal Processing, 30(1), 503-527. [DOI] [Code]
  8. Adam, T. B., Salam, M. S., & Gunawan, T. S. (2013). Wavelet cesptral coefficients for isolated speech recognition. Indonesian Journal of Electrical Engineering and Computer Science, 11(5), 2731-2738. [DOI]
  9. Adam, T. B., & Salam, M. (2012). Spoken english alphabet recognition with mel frequency cepstral coefficients and back propagation neural networks. International Journal of Computer Applications, 42(12), 21-27. [DOI]

Conference Papers

  1. Ren, O. Y., Adam, T., Mohamed, N. S., Hassan, M. F., & Yong, P. Y. (2024, October). Conjugate Momentum Quadratic Penalty Alternating Minimization for Total Variation Image Restoration. In 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 4437-4442). IEEE. [DOI] [Supplementary]
  2. Hassan, M. F., Adam, T., & Paramesran, R. (2023, June). Lightness enhancement method for low illumination night-time image. In AIP Conference Proceedings (Vol. 2756, No. 1). AIP Publishing. [DOI]
  3. Adam, T., Hassan, M. F., & Paramesran, R. (2021, September).A Study on Staircase Artifacts in Total Variation Image Restoration. In 2021 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) (pp. 83-88). IEEE. [DOI] [Code]
  4. Adam, T. B., Salam, M. S., & Gunawan, T. S. (2013, October). Wavelet based Cepstral Coefficients for neural network speech recognition. In 2013 IEEE International Conference on Signal and Image Processing Applications (pp. 447-451). IEEE. [DOI]

Pre-Prints

  1. Adam, T., Malyshev, A., Hassan, M. F., Mohamed, N. S., & Salam, M. S. H. (2023). Accelerated Proximal Iterative re-Weighted $\ell_1 $ Alternating Minimization for Image Deblurring. arXiv preprint arXiv:2309.05204. [arXiv] [Code]
  2. Adam, T. (2024). Manifold Quadratic Penalty Alternating Minimization for Sparse Principal Component Analysis. arXiv preprint arXiv:2411.06654. [arXiv]

Book Chapters

  1. Adam, T., (2025). Variational Methods for Image Restoration. In Frontiers in Image Processing & Computer Vision. Penerbit UTM.
  2. Adam, T., Mohamed, N. S.,Hassan, M. F., & Paramesran, R. (2022). The alternating direction method of multipliers (ADMM) for large-scale convex optimization problems: Applications in image and signal processing. In Operations Research & Analytics in Practice: Theory, Methods & Applications . UTeM Press & MSORSM.

Manuscripts Under Review

  1. Ong, Y. R., Adam, T., & Mohamed, N. S. (2026). A Conjugate-Momentum Quadratic Penalty Alternating Minimization for Total Variation Image Restoration. Submitted to Computational and Applied Mathematics.

For a complete and updated list of publications, please visit:

  1. Google Scholar
  2. ORCID
  3. ResearchGate

Last updated: April 2026