The paper presents convergence guarantees for Plug-and-Play (PnP) image restoration algorithms that use annealed noise levels, covering both deterministic and stochastic methods. It identifies explicit nonconvex objectives linked to the final denoising level and proves that iterates become asymptotically stationary with respect to these objectives, without requiring a specific noise decay schedule. The authors validate their theory experimentally on tasks such as inpainting, super‑resolution, demosaicing, and tomography.
By Samuel Hurault
A blind image denoising framework based on an infinite directional lower semi-frame (DLSF) is introduced for additive white Gaussian noise. The method estimates noise variance directly in the DLSF domain using joint covariance of four directional difference channels, then applies channel‑wise Wiener‑type shrinkage and canonical‑dual synthesis with data‑consistent iterative reconstruction. Experiments on three standard grayscale images show a mean relative noise‑estimation error of 3.28 % and average PSNR gains of 7.45 dB, improving to 8.31 dB at 30/255 noise.
By Hemalatha M, P. Sam Johnson
arXiv:2609.00036v1 Announce Type: new
Abstract: The total scaled-gradient variation (TSGV) regularizer, derived from sparse modeling of piecewise-linear structures, has been shown to preserve edges a...
By Haibin Su, Chunlin Wu, Huibin Chang, Zhifang Liu
arXiv:2609.13409v1 Announce Type: cross
Abstract: Phase unwrapping is a key step in interferometric and coherent imaging, where the physical quantity of interest is carried by a phase that the instru...
By Antoine Moevus, Max Mignotte
arXiv:2609.00036v2 Announce Type: replace
Abstract: The total scaled-gradient variation (TSGV) regularizer, derived from sparse modeling of piecewise-linear structures, has been shown to preserve edg...
By Haibin Su, Chunlin Wu, Huibin Chang, Zhifang Liu
arXiv:2609.25429v1 Announce Type: new
Abstract: In this paper, we introduce the Directional Total Variation-Regularized Implicit Neural Representation (DTV-INR), an advanced variational paradigm that...
By Mahmoud Saeedi Kelishami
arXiv:2607. 14894v1 Announce Type: cross Abstract: Plug-and-play proximal gradient descent (PnP-PGD) enables flexible image reconstruction by using denoisers as implicit priors.
By Guixian Xu, Jinglai Li, Junqi Tang
arXiv:2406. 07435v2 Announce Type: replace-cross Abstract: Image restoration networks are usually comprised of an encoder and a decoder, responsible for aggregating image content from noisy, distorted data and to restore clean, undistorted images, respectively.
By Shashank Agnihotri, Julia Grabinski, Janis Keuper, Margret Keuper
The paper proposes a new covariance model for Denoising Diffusion Probabilistic Models (DDPMs) that captures non‑diagonal correlations and the power‑law frequency spectrum of natural images. Using a Kronecker‑factored DCT (K‑DCT) decomposition, the authors reduce computational complexity from quadratic to log‑linear, enabling efficient sampling with few steps. Experiments on CIFAR‑10, Celeb‑A, ImageNet, and LSUN demonstrate improved FID and likelihoods over previous state‑of‑the‑art samplers.
By Rui Xia, Ayan Das, Artem Artemev, Andi Zhang, Guillaume Hennequin, Alberto Bernacchia
arXiv:2607. 03517v1 Announce Type: new Abstract: Brownian Bridge Diffusion Models (BBDM) offer an appealing framework for image restoration and inverse problems by constructing a stochastic bridge from the clean signal directly to the degraded observation, rather than to pure noise.
By Ron Levi, Michael Elad
arXiv:2607. 25275v1 Announce Type: cross Abstract: Real-world Image Restoration (Real-IR) aims to recover high-quality (HQ) images from complex and unknown degradations.
By Zhenning Shi, Chen Xu, Junhao Zhang, Kefei Zhang, Linjie Liu, Zhedong Zheng, Tao Li
The article reviews how spatially adaptive regularisation weight functions can be incorporated into variational image reconstruction models like Total Variation and Total Generalised Variation. It discusses the regularity properties of these weights—constant, continuous, or piecewise constant—and how they influence edge and detail preservation. The authors highlight recent hybrid methods that learn low‑regularity weights via deep neural networks, demonstrating their effectiveness in image denoising and MRI reconstruction.
By Kostas Papafitsoros, Luca Calatroni, Andreas Kofler