Search Results for author: Fahad Shamshad

Found 12 papers, 3 papers with code

Transformers in Medical Imaging: A Survey

1 code implementation24 Jan 2022 Fahad Shamshad, Salman Khan, Syed Waqas Zamir, Muhammad Haris Khan, Munawar Hayat, Fahad Shahbaz Khan, Huazhu Fu

Following unprecedented success on the natural language tasks, Transformers have been successfully applied to several computer vision problems, achieving state-of-the-art results and prompting researchers to reconsider the supremacy of convolutional neural networks (CNNs) as {de facto} operators.

Image Classification Medical Image Denoising +5

A Survey on Deep Reinforcement Learning for Audio-Based Applications

no code implementations1 Jan 2021 Siddique Latif, Heriberto Cuayáhuitl, Farrukh Pervez, Fahad Shamshad, Hafiz Shehbaz Ali, Erik Cambria

We begin with an introduction to the general field of DL and reinforcement learning (RL), then progress to the main DRL methods and their applications in the audio domain.

Audio Signal Processing reinforcement-learning

Towards an Adversarially Robust Normalization Approach

1 code implementation19 Jun 2020 Muhammad Awais, Fahad Shamshad, Sung-Ho Bae

In this paper, we investigate how BatchNorm causes this vulnerability and proposed new normalization that is robust to adversarial attacks.

Subsampled Fourier Ptychography using Pretrained Invertible and Untrained Network Priors

no code implementations13 May 2020 Fahad Shamshad, Asif Hanif, Ali Ahmed

Recently pretrained generative models have shown promising results for subsampled Fourier Ptychography (FP) in terms of quality of reconstruction for extremely low sampling rate and high noise.

Class-Specific Blind Deconvolutional Phase Retrieval Under a Generative Prior

no code implementations28 Feb 2020 Fahad Shamshad, Ali Ahmed

In this paper, we consider the highly ill-posed problem of jointly recovering two real-valued signals from the phaseless measurements of their circular convolution.

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

Subsampled Fourier Ptychography via Pretrained Invertible and Untrained Network Priors

no code implementations NeurIPS Workshop Deep_Invers 2019 Fahad Shamshad, Asif Hanif, Ali Ahmed

Recently pretrained generative models have shown promising results for subsampled Fourier Ptychography (FP) in terms of quality of reconstruction for extremely low sampling rate and high noise.

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

Deep Ptych: Subsampled Fourier Ptychography using Generative Priors

no code implementations22 Dec 2018 Fahad Shamshad, Farwa Abbas, Ali Ahmed

This paper proposes a novel framework to regularize the highly ill-posed and non-linear Fourier ptychography problem using generative models.

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

Robust Compressive Phase Retrieval via Deep Generative Priors

no code implementations17 Aug 2018 Fahad Shamshad, Ali Ahmed

This paper proposes a new framework to regularize the highly ill-posed and non-linear phase retrieval problem through deep generative priors using simple gradient descent algorithm.

Denoising

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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