141 citations · 766 across the 81 of their papers we have counts for
13 papers · 1 filter
Laplacian-guided Entropy Model in Neural Codec with Blur-dissipated Synthesis
Atefeh Khoshkhahtinat, Ali Zafari, Piyush M. Mehta +1
While replacing Gaussian decoders with a conditional diffusion model enhances the perceptual quality of reconstructions in neural image compression, their lack of inductive bias fo…
Neural-based Compression Scheme for Solar Image Data
Ali Zafari, Atefeh Khoshkhahtinat, Jeremy A. Grajeda +6
Studying the solar system and especially the Sun relies on the data gathered daily from space missions. These missions are data-intensive and compressing this data to make them eff…
Multi-spectral Entropy Constrained Neural Compression of Solar Imagery
Ali Zafari, Atefeh Khoshkhahtinat, Piyush M. Mehta +4
Missions studying the dynamic behaviour of the Sun are defined to capture multi-spectral images of the sun and transmit them to the ground station in a daily basis. To make transmi…
Multi-Context Dual Hyper-Prior Neural Image Compression
Atefeh Khoshkhahtinat, Ali Zafari, Piyush M. Mehta +3
Transform and entropy models are the two core components in deep image compression neural networks. Most existing learning-based image compression methods utilize convolutional-bas…
Context-Aware Neural Video Compression on Solar Dynamics Observatory
Atefeh Khoshkhahtinat, Ali Zafari, Piyush M. Mehta +4
NASA's Solar Dynamics Observatory (SDO) mission collects large data volumes of the Sun's daily activity. Data compression is crucial for space missions to reduce data storage and v…
Frequency Disentangled Features in Neural Image Compression
Ali Zafari, Atefeh Khoshkhahtinat, Piyush Mehta +3
The design of a neural image compression network is governed by how well the entropy model matches the true distribution of the latent code. Apart from the model capacity, this abi…
Attention-Based Generative Neural Image Compression on Solar Dynamics Observatory
Ali Zafari, Atefeh Khoshkhahtinat, Piyush M. Mehta +4
NASA's Solar Dynamics Observatory (SDO) mission gathers 1.4 terabytes of data each day from its geosynchronous orbit in space. SDO data includes images of the Sun captured at diffe…
GAN-based Super-Resolution and Segmentation of Retinal Layers in Optical coherence tomography Scans
Paria Jeihouni, Omid Dehzangi, Annahita Amireskandari +2
In this paper, we design a Generative Adversarial Network (GAN)-based solution for super-resolution and segmentation of optical coherence tomography (OCT) scans of the retinal laye…
Superresolution and Segmentation of OCT scans using Multi-Stage adversarial Guided Attention Training
Paria Jeihouni, Omid Dehzangi, Annahita Amireskandari +3
Optical coherence tomography (OCT) is one of the non-invasive and easy-to-acquire biomarkers (the thickness of the retinal layers, which is detectable within OCT scans) being inves…
Unsupervised Pixel-wise Hyperspectral Anomaly Detection via Autoencoding Adversarial Networks
Sertac Arisoy, Nasser M. Nasrabadi, Koray Kayabol
We propose a completely unsupervised pixel-wise anomaly detection method for hyperspectral images. The proposed method consists of three steps called data preparation, reconstructi…
Efficient OCT Image Segmentation Using Neural Architecture Search
Saba Heidari Gheshlaghi, Omid Dehzangi, Ali Dabouei +3
In this work, we propose a Neural Architecture Search (NAS) for retinal layer segmentation in Optical Coherence Tomography (OCT) scans. We incorporate the Unet architecture in the…
GAN-based Hyperspectral Anomaly Detection
Sertac Arisoy, Nasser M. Nasrabadi, Koray Kayabol
In this paper, we propose a generative adversarial network (GAN)-based hyperspectral anomaly detection algorithm. In the proposed algorithm, we train a GAN model to generate a synt…
A data-driven proxy to Stoke's flow in porous media
Ali Takbiri-Borujeni, Hadi Kazemi, Nasser Nasrabadi
The objective for this work is to develop a data-driven proxy to high-fidelity numerical flow simulations using digital images. The proposed model can capture the flow field and pe…