Subhadip Mukherjee | DeepAI

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∙ 08/19/2023

Dynamic Bilevel Learning with Inexact Line Search

In various domains within imaging and data science, particularly when ad...

0 Mohammad Sadegh Salehi, et al. ∙

∙ 07/18/2023

Convergent regularization in inverse problems and linear plug-and-play denoisers

Plug-and-play (PnP) denoising is a popular iterative framework for solvi...

0 Andreas Hauptmann, et al. ∙

∙ 04/17/2023

NF-ULA: Langevin Monte Carlo with Normalizing Flow Prior for Imaging Inverse Problems

Bayesian methods for solving inverse problems are a powerful alternative...

0 Ziruo Cai, et al. ∙

∙ 03/20/2023

Fluctuation-based deconvolution in fluorescence microscopy using plug-and-play denoisers

The spatial resolution of images of living samples obtained by fluoresce...

0 Vasiliki Stergiopoulou, et al. ∙

∙ 03/09/2023

Provably Convergent Plug-and-Play Quasi-Newton Methods

Plug-and-Play (PnP) methods are a class of efficient iterative methods t...

0 Hong Ye Tan, et al. ∙

∙ 02/24/2023

Implicit neural representations for unsupervised super-resolution and denoising of 4D flow MRI

4D flow MRI is a non-invasive imaging method that can measure blood flow...

0 Simone Saitta, et al. ∙

∙ 08/31/2022

Accelerating Deep Unrolling Networks via Dimensionality Reduction

In this work we propose a new paradigm for designing efficient deep unro...

11 Junqi Tang, et al. ∙

∙ 08/18/2022

Tree species classification from hyperspectral data using graph-regularized neural networks

Manual labeling of tree species remains a challenging task, especially i...

0 Debmita Bandyopadhyay, et al. ∙

∙ 06/11/2022

Learned reconstruction with convergence guarantees

In recent years, deep learning has achieved remarkable empirical success...

16 Subhadip Mukherjee, et al. ∙

∙ 10/24/2021

Learning convex regularizers satisfying the variational source condition for inverse problems

Variational regularization has remained one of the most successful appro...

0 Subhadip Mukherjee, et al. ∙

∙ 10/07/2021

StyleGAN-induced data-driven regularization for inverse problems

Recent advances in generative adversarial networks (GANs) have opened up...

0 Arthur Conmy, et al. ∙

∙ 06/07/2021

End-to-end reconstruction meets data-driven regularization for inverse problems

We propose an unsupervised approach for learning end-to-end reconstructi...

0 Subhadip Mukherjee, et al. ∙

∙ 03/30/2021

Adversarially learned iterative reconstruction for imaging inverse problems

In numerous practical applications, especially in medical image reconstr...

0 Subhadip Mukherjee, et al. ∙

∙ 08/06/2020

Learned convex regularizers for inverse problems

We consider the variational reconstruction framework for inverse problem...

0 Subhadip Mukherjee, et al. ∙

∙ 10/02/2018

Quantization-Aware Phase Retrieval

We address the problem of phase retrieval (PR) from quantized measuremen...

8 Subhadip Mukherjee, et al. ∙

∙ 08/26/2014

ℓ_1-K-SVD: A Robust Dictionary Learning Algorithm With Simultaneous Update

We develop a dictionary learning algorithm by minimizing the ℓ_1 distort...

0 Subhadip Mukherjee, et al. ∙

∙ 03/19/2014

A Split-and-Merge Dictionary Learning Algorithm for Sparse Representation

In big data image/video analytics, we encounter the problem of learning ...

0 Subhadip Mukherjee, et al. ∙

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