no code implementations • 29 Jul 2023 • Muhammad Asim, Muhammad Ozair Iqbal, Waqas Aman, Muhammad Mahboob Ur Rahman, Qammer H. Abbasi
It turns out that the performance of the ML algorithms is only slightly superior to the Neyman-Pearson-based binary hypothesis test (BHT).
no code implementations • 27 Jul 2020 • Muhammad Asim, Yong Wang, Kezhi Wang, Pei-Qiu Huang
These optimization problems usually have complex properties, such as non-convexity and NP-hardness, which may not be addressed by the traditional convex optimization-based solutions.
no code implementations • 1 Mar 2020 • Muhammad Asim, Muaaz Zakria
k-nearest neighbour (kNN) is one of the most prominent, simple and basic algorithm used in machine learning and data mining.
no code implementations • NeurIPS Workshop Deep_Invers 2019 • Muhammad Asim, Fahad Shamshad, Ali Ahmed
In this work, we show that this strong prior, enforced by the structure of a ConvNet, can be augmented with the information that recurs in different patches of a natural image to boost the performance.
1 code implementation • 20 Aug 2019 • Muhammad Asim, Fahad Shamshad, Ali Ahmed
This paper proposes a novel approach to regularize the ill-posed blind image deconvolution (blind image deblurring) problem using deep generative networks.
1 code implementation • 28 May 2019 • Muhammad Asim, Max Daniels, Oscar Leong, Ali Ahmed, Paul Hand
For compressive sensing, invertible priors can yield higher accuracy than sparsity priors across almost all undersampling ratios, and due to their lack of representation error, invertible priors can yield better reconstructions than GAN priors for images that have rare features of variation within the biased training set, including out-of-distribution natural images.
no code implementations • 20 Feb 2019 • Muhammad Usman, Muhammad Umar Farooq, Siddique Latif, Muhammad Asim, Junaid Qadir
The downside of multishot MRI is that it is very sensitive to subject motion and even small amounts of motion during the scan can produce artifacts in the final MR image that may cause misdiagnosis.
Generative Adversarial Network Motion Correction In Multishot Mri +1
1 code implementation • 29 Nov 2018 • Fahad Shamshad, Muhammad Awais, Muhammad Asim, Zain ul Aabidin Lodhi, Muhammad Umair, Ali Ahmed
Among the plethora of techniques devised to curb the prevalence of noise in medical images, deep learning based approaches have shown the most promise.
no code implementations • 24 Nov 2018 • Siddique Latif, Muhammad Asim, Muhammad Usman, Junaid Qadir, Rajib Rana
Multishot Magnetic Resonance Imaging (MRI) has recently gained popularity as it accelerates the MRI data acquisition process without compromising the quality of final MR image.
1 code implementation • 12 Feb 2018 • Muhammad Asim, Fahad Shamshad, Ali Ahmed
This paper proposes a novel approach to regularize the \textit{ill-posed} and \textit{non-linear} blind image deconvolution (blind deblurring) using deep generative networks as priors.