scispace - formally typeset
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
More filters
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

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.