M
M. Omair Ahmad
Researcher at Concordia University
Publications - 248
Citations - 2691
M. Omair Ahmad is an academic researcher from Concordia University. The author has contributed to research in topics: Wavelet & Noise. The author has an hindex of 24, co-authored 247 publications receiving 2066 citations. Previous affiliations of M. Omair Ahmad include Concordia University Wisconsin.
Papers
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Proceedings ArticleDOI
A novel transform for image compression
TL;DR: An orthogonal multiplication-free transform of order that is an integral power of two by an appropriate extension of the well-known fourthorder integer discrete cosine transform is proposed and an efficient algorithm for its fast computation is developed.
Journal ArticleDOI
A Robust Multibit Multiplicative Watermark Decoder Using a Vector-Based Hidden Markov Model in Wavelet Domain
TL;DR: This paper proposes a scheme for designing a blind multibit watermark decoder incorporating the vector-based HMM in wavelet domain and shows that the proposed decoder is more robust against various kinds of attacks compared with the state-of-the-art methods.
Journal ArticleDOI
Multi-Site Infant Brain Segmentation Algorithms: The iSeg-2019 Challenge
Yue Sun,Kun Gao,Zhengwang Wu,Zhihao Lei,Ying Wei,Jun Ma,Xiaoping Yang,Xue Feng,Li Zhao,Trung Le Phan,Jitae Shin,Tao Zhong,Yu Zhang,Lequan Yu,Caizi Li,Ramesh Basnet,M. Omair Ahmad,Mallappa Kumara Swamy,Wenao Ma,Qi Dou,Toan Duc Bui,Camilo Bermudez Noguera,Bennett A. Landman,Ian H. Gotlib,Kathryn L. Humphreys,Sarah Shultz,Longchuan Li,Sijie Niu,Weili Lin,Valerie Jewells,Gang Li,Dinggang Shen,Li Wang +32 more
TL;DR: 8 top-ranked methods in the iSeg-2019 challenge were reviewed by detailing their pipelines/implementations, presenting experimental results, and evaluating performance across different sites in terms of whole brain, regions of interest, and gyral landmark curves, finding that multi-site consistency is still an open issue.
Journal ArticleDOI
Contrast-based fusion of noisy images using discrete wavelet transform
TL;DR: Results show that the performance of the proposed fusion method is better than that of other methods in terms of several frequently-used metrics, such as the structural similarity, peak signal-to-noise ratio and cross-entropy, as well as in the visual quality, even in the case of correlated noise.
Journal ArticleDOI
Mixed Gaussian-impulse noise reduction from images using convolutional neural network
TL;DR: Experimental results on different settings of mixed-noise show that the proposed CNN-based denoising method performs significantly better than the sparse representation and patch-based methods do both in terms of accuracy and robustness.