51 citations · 166 across the 24 of their papers we have counts for
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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…
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…
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…