167 citations · 962 across the 54 of their papers we have counts for
59 papers
Stabilizing Deep Reconstruction Operators with Contractive Anchoring
Arghya Sinha, Trishit Mukherjee, Kunal N. Chaudhury
Pretrained deep denoisers can be used to solve a wide range of model-based image reconstruction tasks via Plug-and-Play (PnP) and Regularization-by-Denoising (RED) algorithms, with…
Trainable Nonexpansive Denoisers for Contractive Image Reconstruction
Arghya Sinha, Aditya Banerjee, Trishit Mukherjee +1
Trainable denoisers with Lipschitz control have become central to convergent image reconstruction. However, training neural networks that simultaneously offer strong denoising perf…
Local-Minima-Preserving Continuous Relaxation of Ising Problems
Debraj Banerjee, Santanu Mahapatra, Kunal N. Chaudhury
The generalized Ising problem captures a broad spectrum of hard combinatorial problems, including MAX-CUT, Number Partitioning (NPP), and Maximum Independent Set. In this work, we…
On the Schur Stability of Some Image Reconstruction Operators
Debraj Banerjee, Kunal Narayan Chaudhury
We investigate an open problem arising in iterative image reconstruction. In its general form, the problem is to determine the stability of the parametric family of operators $P(t)…
Stabilizing RED using the Koopman Operator
Shraddha Chavan, Kunal N. Chaudhury
The widely used RED (Regularization-by-Denoising) framework uses pretrained denoisers as implicit regularizers for model-based reconstruction. Although RED generally yields high-fi…
HyDeFuse: Provably Convergent Denoiser-Driven Hyperspectral Fusion
Sagar Kumar, Unni V S, Kunal Narayan Chaudhury
Hyperspectral (HS) images provide fine spectral resolution but have limited spatial resolution, whereas multispectral (MS) images capture finer spatial details but have fewer bands…
A Continuous Energy Ising Machine Leveraging Difference-of-Convex Programming
Debraj Banerjee, Santanu Mahapatra, Kunal Narayan Chaudhury
Many combinatorial optimization problems can be reformulated as finding the ground state of the Ising model. Existing Ising solvers are mostly inspired by simulated annealing. Alth…
Viscosity Stabilized Plug-and-Play Reconstruction
Arghya Sinha, Trishit Mukherjee, Kunal N. Chaudhury
The plug-and-play (PnP) method uses a deep denoiser within a proximal algorithm for model-based image reconstruction (IR). Unlike end-to-end IR, PnP allows the same pretrained deno…
Linear Convergence of Plug-and-Play Algorithms with Kernel Denoisers
Arghya Sinha, Bhartendu Kumar, Chirayu D. Athalye +1
The use of denoisers for image reconstruction has shown significant potential, especially for the Plug-and-Play (PnP) framework. In PnP, a powerful denoiser is used as an implicit…
FISTA Iterates Converge Linearly for Denoiser-Driven Regularization
Arghya Sinha, Kunal N. Chaudhury
The effectiveness of denoising-driven regularization for image reconstruction has been widely recognized. Two prominent algorithms in this area are Plug-and-Play () a…
On the Strong Convexity of PnP Regularization Using Linear Denoisers
Arghya Sinha, Kunal N Chaudhury
In the Plug-and-Play (PnP) method, a denoiser is used as a regularizer within classical proximal algorithms for image reconstruction. It is known that a broad class of linear denoi…
On the Contractivity of Plug-and-Play Operators
Chirayu D. Athalye, Kunal N. Chaudhury, Bhartendu Kumar
In plug-and-play (PnP) regularization, the proximal operator in algorithms such as ISTA and ADMM is replaced by a powerful denoiser. This formal substitution works surprisingly wel…
Compressive Sensing of ECG Signals using Plug-and-Play Regularization
Unni VS, Ruturaj Gavaskar, Kunal Narayan Chaudhury
Compressive Sensing (CS) has recently attracted attention for ECG data compression. In CS, an ECG signal is projected onto a small set of random vectors. Recovering the original si…
Guided Nonlocal Patch Regularization and Efficient Filtering-Based Inversion for Multiband Fusion
Unni V. S., Pravin Nair, Kunal N. Chaudhury
In multiband fusion, an image with a high spatial and low spectral resolution is combined with an image with a low spatial but high spectral resolution to produce a single multiban…
Plug-and-Play Regularization using Linear Solvers
Pravin Nair, Kunal N. Chaudhury
There has been tremendous research on the design of image regularizers over the years, from simple Tikhonov and Laplacian to sophisticated sparsity and CNN-based regularizers. Coup…
On Exact and Robust Recovery for Plug-and-Play Compressed Sensing
Ruturaj G. Gavaskar, Chirayu D. Athalye, Kunal N. Chaudhury
In Plug-and-Play (PnP) algorithms, an off-the-shelf denoiser is used for image regularization. PnP yields state-of-the-art results, but its theoretical aspects are not well underst…
Averaged Deep Denoisers for Image Regularization
Pravin Nair, Kunal N. Chaudhury
Plug-and-Play (PnP) and Regularization-by-Denoising (RED) are recent paradigms for image reconstruction that leverage the power of modern denoisers for image regularization. In par…
On Plug-and-Play Regularization using Linear Denoisers
Ruturaj G. Gavaskar, Chirayu D. Athalye, Kunal N. Chaudhury
In plug-and-play (PnP) regularization, the knowledge of the forward model is combined with a powerful denoiser to obtain state-of-the-art image reconstructions. This is typically d…
Fixed-Point and Objective Convergence of Plug-and-Play Algorithms
Pravin Nair, Ruturaj G. Gavaskar, Kunal N. Chaudhury
A standard model for image reconstruction involves the minimization of a data-fidelity term along with a regularizer, where the optimization is performed using proximal algorithms…
Plug-and-play ISTA converges with kernel denoisers
Ruturaj G. Gavaskar, Kunal N. Chaudhury
Plug-and-play (PnP) method is a recent paradigm for image regularization, where the proximal operator (associated with some given regularizer) in an iterative algorithm is replaced…
On the Proof of Fixed-Point Convergence for Plug-and-Play ADMM
Ruturaj G. Gavaskar, Kunal N. Chaudhury
In most state-of-the-art image restoration methods, the sum of a data-fidelity and a regularization term is optimized using an iterative algorithm such as ADMM (alternating directi…
Convergence Analysis of Nonconvex ADMM for Rigid Registration
Aditya V. Singh, Kunal N. Chaudhury
We consider the problem of rigid registration, where we wish to jointly register multiple point sets via rigid transforms. This arises in applications such as sensor network locali…
On Uniquely Registrable Networks
Aditya V. Singh, Kunal N. Chaudhury
Consider a network with nodes in -dimensional Euclidean space, and subsets of these nodes . Assume that the nodes in a given are observed in a loca…
Least-squares registration of point sets over SE (d) using closed-form projections
Sk. Miraj Ahmed, Niladri Ranjan Das, Kunal Narayan Chaudhury
Consider the problem of registering multiple point sets in some -dimensional space using rotations and translations. Assume that there are sets with common points, and moreover…