11 papers
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 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…
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…
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…
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…
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)…