Search Results for author:
Found 10 papers, 4 papers with code
Pathloss-based non-Line-of-Sight Identification in an Indoor Environment: An Experimental Study
It turns out that the performance of the ML algorithms is only slightly superior to the Neyman-Pearson-based binary hypothesis test (BHT).
A Review on Computational Intelligence Techniques in Cloud and Edge Computing
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.
Advanced kNN: A Mature Machine Learning Series
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
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.
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.
Invertible generative models for inverse problems: mitigating representation error and dataset bias
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.
Motion Corrected Multishot MRI Reconstruction Using Generative Networks with Sensitivity Encoding
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
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.
Automating Motion Correction in Multishot MRI Using Generative Adversarial Networks
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.
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.
