arXiv Computer Vision
Adaptive double-phase Rudin--Osher--Fatemi denoising model
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Convergent Plug-and-Play Image Restoration with Annealed Noise Levels
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.
arXiv Computer Vision
Sep 24
Image Denoising Using Lower Semi-Frames
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.
arXiv Computer Vision
Sep 2
A Cone-Constrained Bilinear Decomposition for Total Scaled-Gradient Variation Models
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...
arXiv Computer Vision
Sep 15
Translation-Invariant Tile-Based Phase Unwrapping with Residual-Weighted Multipath Averaging
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...
arXiv Computer Vision
Sep 15
A Globally Convergent Algorithm for Total Scaled-Gradient Variation via Cone-Constrained Bilinear Decomposition
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...
arXiv Computer Vision
Sep 23
Directional Total Variation-Regularized Implicit Neural Representations (DTV-INR) for Continuous Super-Resolution in Degraded Imaging Domains
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...