Search Results for author: Motaz Alfarraj

Found 13 papers, 7 papers with code

Recursions Are All You Need: Towards Efficient Deep Unfolding Networks

1 code implementation9 May 2023 Rawwad Alhejaili, Motaz Alfarraj, Hamzah Luqman, Ali Al-Shaikhi

Secondly, we randomize the number of recursions during training to decrease the overall training time.

Compressive Sensing

Meta-Optimization of Deep CNN for Image Denoising Using LSTM

no code implementations14 Jul 2021 Basit O. Alawode, Motaz Alfarraj

The recent application of deep learning (DL) to various tasks has seen the performance of classical techniques surpassed by their DL-based counterparts.

Image Denoising

Spatiotemporal Modeling of Seismic Images for Acoustic Impedance Estimation

no code implementations28 Jun 2020 Ahmad Mustafa, Motaz Alfarraj, Ghassan AlRegib

We empirically compare our proposed workflow with some other sequence modeling-based neural networks that model seismic data only temporally.

Seismic Inversion

Fabric Surface Characterization: Assessment of Deep Learning-based Texture Representations Using a Challenging Dataset

no code implementations16 Mar 2020 Yuting Hu, Zhiling Long, Anirudha Sundaresan, Motaz Alfarraj, Ghassan AlRegib, Sungmee Park, Sundaresan Jayaraman

We formulate the problem as a very fine-grained texture classification problem, and study how deep learning-based texture representation techniques can help tackle the task.

Material Recognition Object Recognition +2

Estimation of Acoustic Impedance from Seismic Data using Temporal Convolutional Network

3 code implementations6 Jun 2019 Ahmad Mustafa, Motaz Alfarraj, Ghassan AlRegib

In exploration seismology, seismic inversion refers to the process of inferring physical properties of the subsurface from seismic data.

Seismic Inversion

Semi-supervised Learning for Acoustic Impedance Inversion

2 code implementations31 May 2019 Motaz Alfarraj, Ghassan AlRegib

Then, a neural-network-based inversion model comprising convolutional and recurrent neural layers is used to invert seismic data for acoustic impedance.

Image and Video Processing Signal Processing Geophysics

Structure Label Prediction Using Similarity-Based Retrieval and Weakly-Supervised Label Mapping

no code implementations16 May 2019 Yazeed Alaudah, Motaz Alfarraj, Ghassan AlRegib

By having an interpreter select a very small number of exemplar images for every class of subsurface structures, we use a novel similarity-based retrieval technique to extract thousands of images that contain similar subsurface structures from the seismic volume.

BIG-bench Machine Learning Retrieval +1

Multiresolution Analysis and Learning for Computational Seismic Interpretation

1 code implementation24 Jan 2019 Motaz Alfarraj, Yazeed Alaudah, Zhiling Long, Ghassan AlRegib

Moreover, directional multiresolution attributes, such as the curvelet transform, are more effective than the non-directional attributes in distinguishing different subsurface structures in large seismic datasets, and can greatly help the interpretation process.

Image and Video Processing Geophysics

Petrophysical Property Estimation from Seismic Data Using Recurrent Neural Networks

no code implementations24 Jan 2019 Motaz Alfarraj, Ghassan AlRegib

Recent advances in machine learning have shown promising results for recurrent neural networks (RNN) in modeling complex sequential data such as videos and speech signals.

BIG-bench Machine Learning regression

A Machine Learning Benchmark for Facies Classification

5 code implementations12 Jan 2019 Yazeed Alaudah, Patrycja Michalowicz, Motaz Alfarraj, Ghassan AlRegib

In addition to making the dataset and the code publicly available, this work helps advance research in this area by creating an objective benchmark for comparing the results of different machine learning approaches for facies classification.

BIG-bench Machine Learning Classification +2

Content-adaptive non-parametric texture similarity measure

1 code implementation5 Nov 2018 Motaz Alfarraj, Yazeed Alaudah, Ghassan AlRegib

In this paper, we introduce a non-parametric texture similarity measure based on the singular value decomposition of the curvelet coefficients followed by a content-based truncation of the singular values.

Image and Video Processing

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