Search Results for author: Subhadip Mukherjee

Found 24 papers, 5 papers with code

Weakly Convex Regularisers for Inverse Problems: Convergence of Critical Points and Primal-Dual Optimisation

no code implementations1 Feb 2024 Zakhar Shumaylov, Jeremy Budd, Subhadip Mukherjee, Carola-Bibiane Schönlieb

Variational regularisation is the primary method for solving inverse problems, and recently there has been considerable work leveraging deeply learned regularisation for enhanced performance.

Computed Tomography (CT)

Unsupervised approaches based on optimal transport and convex analysis for inverse problems in imaging

no code implementations15 Nov 2023 Marcello Carioni, Subhadip Mukherjee, Hong Ye Tan, Junqi Tang

Together with a detailed survey, we provide an overview of the key mathematical results that underlie the methods reviewed in the chapter to keep our discussion self-contained.

Provably Convergent Data-Driven Convex-Nonconvex Regularization

no code implementations9 Oct 2023 Zakhar Shumaylov, Jeremy Budd, Subhadip Mukherjee, Carola-Bibiane Schönlieb

An emerging new paradigm for solving inverse problems is via the use of deep learning to learn a regularizer from data.

Dynamic Bilevel Learning with Inexact Line Search

no code implementations19 Aug 2023 Mohammad Sadegh Salehi, Subhadip Mukherjee, Lindon Roberts, Matthias J. Ehrhardt

We show convergence to a stationary point of the loss with respect to hyperparameters.

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

no code implementations18 Jul 2023 Andreas Hauptmann, Subhadip Mukherjee, Carola-Bibiane Schönlieb, Ferdia Sherry

While a significant amount of research has gone into establishing the convergence of the PnP iteration for different regularity conditions on the denoisers, not much is known about the asymptotic properties of the converged solution as the noise level in the measurement tends to zero, i. e., whether PnP methods are provably convergent regularization schemes under reasonable assumptions on the denoiser.

Denoising Image Reconstruction

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

1 code implementation17 Apr 2023 Ziruo Cai, Junqi Tang, Subhadip Mukherjee, Jinglai Li, Carola Bibiane Schönlieb, Xiaoqun Zhang

Bayesian methods for solving inverse problems are a powerful alternative to classical methods since the Bayesian approach offers the ability to quantify the uncertainty in the solution.

Bayesian Inference Computed Tomography (CT) +4

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

no code implementations20 Mar 2023 Vasiliki Stergiopoulou, Subhadip Mukherjee, Luca Calatroni, Laure Blanc-Féraud

The spatial resolution of images of living samples obtained by fluorescence microscopes is physically limited due to the diffraction of visible light, which makes the study of entities of size less than the diffraction barrier (around 200 nm in the x-y plane) very challenging.

Denoising Super-Resolution

Provably Convergent Plug-and-Play Quasi-Newton Methods

1 code implementation9 Mar 2023 Hong Ye Tan, Subhadip Mukherjee, Junqi Tang, Carola-Bibiane Schönlieb

Plug-and-Play (PnP) methods are a class of efficient iterative methods that aim to combine data fidelity terms and deep denoisers using classical optimization algorithms, such as ISTA or ADMM, with applications in inverse problems and imaging.

Deblurring Image Deblurring +1

Accelerating Deep Unrolling Networks via Dimensionality Reduction

no code implementations31 Aug 2022 Junqi Tang, Subhadip Mukherjee, Carola-Bibiane Schönlieb

In this work we propose a new paradigm for designing efficient deep unrolling networks using dimensionality reduction schemes, including minibatch gradient approximation and operator sketching.

Dimensionality Reduction Image Reconstruction +1

Operator Sketching for Deep Unrolling Networks

no code implementations21 Mar 2022 Junqi Tang, Subhadip Mukherjee, Carola-Bibiane Schönlieb

In this work we propose a new paradigm for designing efficient deep unrolling networks using operator sketching.

Image Reconstruction Rolling Shutter Correction

Learning convex regularizers satisfying the variational source condition for inverse problems

no code implementations NeurIPS Workshop Deep_Invers 2021 Subhadip Mukherjee, Carola-Bibiane Schönlieb, Martin Burger

Variational regularization has remained one of the most successful approaches for reconstruction in imaging inverse problems for several decades.

Stochastic Primal-Dual Deep Unrolling

no code implementations19 Oct 2021 Junqi Tang, Subhadip Mukherjee, Carola-Bibiane Schönlieb

We develop a stochastic (ordered-subsets) variant of the classical learned primal-dual (LPD), which is a state-of-the-art unrolling network for tomographic image reconstruction.

Computational Efficiency Computed Tomography (CT) +2

StyleGAN-induced data-driven regularization for inverse problems

no code implementations7 Oct 2021 Arthur Conmy, Subhadip Mukherjee, Carola-Bibiane Schönlieb

Our proposed approach, which we refer to as learned Bayesian reconstruction with generative models (L-BRGM), entails joint optimization over the style-code and the input latent code, and enhances the expressive power of a pre-trained StyleGAN2 generator by allowing the style-codes to be different for different generator layers.

Image Inpainting Image Reconstruction +1

Adversarially learned iterative reconstruction for imaging inverse problems

1 code implementation30 Mar 2021 Subhadip Mukherjee, Ozan Öktem, Carola-Bibiane Schönlieb

In numerous practical applications, especially in medical image reconstruction, it is often infeasible to obtain a large ensemble of ground-truth/measurement pairs for supervised learning.

Image Reconstruction

Learned convex regularizers for inverse problems

1 code implementation6 Aug 2020 Subhadip Mukherjee, Sören Dittmer, Zakhar Shumaylov, Sebastian Lunz, Ozan Öktem, Carola-Bibiane Schönlieb

We consider the variational reconstruction framework for inverse problems and propose to learn a data-adaptive input-convex neural network (ICNN) as the regularization functional.

Computed Tomography (CT) Deblurring

Quantization-Aware Phase Retrieval

no code implementations2 Oct 2018 Subhadip Mukherjee, Chandra Sekhar Seelamantula

A comparison with the state-of-the- art algorithms shows that the proposed algorithm has a higher reconstruction accuracy and is about 2 to 3 dB away from the CRB.

Quantization Retrieval

Online Reweighted Least Squares Algorithm for Sparse Recovery and Application to Short-Wave Infrared Imaging

no code implementations29 Jun 2017 Subhadip Mukherjee, Deepak R., Huaijin Chen, Ashok Veeraraghavan, Chandra Sekhar Seelamantula

The proposed online algorithm is useful in a setting where one seeks to design a progressive decoding strategy to reconstruct a sparse signal from linear measurements so that one does not have to wait until all measurements are acquired.

Deep Sparse Coding Using Optimized Linear Expansion of Thresholds

no code implementations20 May 2017 Debabrata Mahapatra, Subhadip Mukherjee, Chandra Sekhar Seelamantula

We address the problem of reconstructing sparse signals from noisy and compressive measurements using a feed-forward deep neural network (DNN) with an architecture motivated by the iterative shrinkage-thresholding algorithm (ISTA).

Image Denoising

$\ell_1$-K-SVD: A Robust Dictionary Learning Algorithm With Simultaneous Update

no code implementations26 Aug 2014 Subhadip Mukherjee, Rupam Basu, Chandra Sekhar Seelamantula

We develop a dictionary learning algorithm by minimizing the $\ell_1$ distortion metric on the data term, which is known to be robust for non-Gaussian noise contamination.

Denoising Dictionary Learning +1

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

no code implementations19 Mar 2014 Subhadip Mukherjee, Chandra Sekhar Seelamantula

We show that the proposed algorithm is efficient in its usage of memory and computational complexity, and performs on par with the standard learning strategy operating on the entire data at a time.

Dictionary Learning Image Denoising

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