3 citations · 3 across the 5 of their papers we have counts for
6 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…
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…