Search Results for author:
Found 15 papers, 9 papers with code
Blind Image Deconvolution using Deep Generative Priors
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.
Robust Compressive Phase Retrieval via Deep Generative Priors
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.
Leveraging Deep Stein's Unbiased Risk Estimator for Unsupervised X-ray Denoising
Among the plethora of techniques devised to curb the prevalence of noise in medical images, deep learning based approaches have shown the most promise.
Deep Ptych: Subsampled Fourier Ptychography using Generative Priors
This paper proposes a novel framework to regularize the highly ill-posed and non-linear Fourier ptychography problem using generative models.
Blind Image Deconvolution using Pretrained Generative Priors
This paper proposes a novel approach to regularize the ill-posed blind image deconvolution (blind image deblurring) problem using deep generative networks.
PatchDIP Exploiting Patch Redundancy in Deep Image Prior for Denoising
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.
Subsampled Fourier Ptychography via Pretrained Invertible and Untrained Network Priors
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
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.
Subsampled Fourier Ptychography using Pretrained Invertible and Untrained Network Priors
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.
Towards an Adversarially Robust Normalization Approach
In this paper, we investigate how BatchNorm causes this vulnerability and proposed new normalization that is robust to adversarial attacks.
A Survey on Deep Reinforcement Learning for Audio-Based Applications
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.
Transformers in Medical Imaging: A Survey
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.
Transformers in Speech Processing: A Survey
The remarkable success of transformers in the field of natural language processing has sparked the interest of the speech-processing community, leading to an exploration of their potential for modeling long-range dependencies within speech sequences.
CLIP2Protect: Protecting Facial Privacy using Text-Guided Makeup via Adversarial Latent Search
We propose a novel two-step approach for facial privacy protection that relies on finding adversarial latent codes in the low-dimensional manifold of a pretrained generative model.
Evading Forensic Classifiers with Attribute-Conditioned Adversarial Faces
While these forensic models can detect whether a face image is synthetic or real with high accuracy, they are also vulnerable to adversarial attacks.
