Search Results for author: Muhammad Asim

Found 9 papers, 2 papers with code

A Review on Computational Intelligence Techniques in Cloud and Edge Computing

no code implementations27 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.

Edge-computing

Advanced kNN: A Mature Machine Learning Series

no code implementations1 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.

PatchDIP Exploiting Patch Redundancy in Deep Image Prior for Denoising

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.

Denoising

Blind Image Deconvolution using Pretrained Generative Priors

no code implementations20 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.

Blind Image Deblurring Image Deblurring +1

Invertible generative models for inverse problems: mitigating representation error and dataset bias

1 code implementation28 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.

Compressive Sensing Denoising

Motion Corrected Multishot MRI Reconstruction Using Generative Networks with Sensitivity Encoding

no code implementations20 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.

Motion Correction In Multishot Mri MRI Reconstruction

Leveraging Deep Stein's Unbiased Risk Estimator for Unsupervised X-ray Denoising

no code implementations29 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.

Denoising

Automating Motion Correction in Multishot MRI Using Generative Adversarial Networks

no code implementations24 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.

Image Reconstruction Motion Correction In Multishot Mri

Blind Image Deconvolution using Deep Generative Priors

1 code implementation12 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.

Deblurring Image Deblurring +1

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