Publications (15)
Pathloss-based non-Line-of-Sight Identification in an Indoor Environment: An Experimental Study
Muhammad Asim, Muhammad Ozair Iqbal, Waqas Aman +2
This paper reports the findings of an experimental study on the problem of line-of-sight (LOS)/non-line-of-sight (NLOS) classification in an indoor environment. Specifically, we de…
Blind Image Deconvolution using Pretrained Generative Priors
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. We employ two separate d…
Invertible generative models for inverse problems: mitigating representation error and dataset bias
Muhammad Asim, Mara Daniels, Oscar Leong +2
Trained generative models have shown remarkable performance as priors for inverse problems in imaging -- for example, Generative Adversarial Network priors permit recovery of test…
DistillGrasp: Integrating Features Correlation with Knowledge Distillation for Depth Completion of Transparent Objects
Yiheng Huang, Junhong Chen, Nick Michiels +3
Due to the visual properties of reflection and refraction, RGB-D cameras cannot accurately capture the depth of transparent objects, leading to incomplete depth maps. To fill in th…
Augmenting Generative Adversarial Networks for Speech Emotion Recognition
Siddique Latif, Muhammad Asim, Rajib Rana +3
Generative adversarial networks (GANs) have shown potential in learning emotional attributes and generating new data samples. However, their performance is usually hindered by the…
Blind Image Deconvolution using Deep Generative Priors
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 pri…