The paper introduces a tensor‑based functional for targeted image enhancement and denoising, incorporating application‑dependent and contextual information through explicit regularization. It establishes existence of a minimizer and discusses tensor symmetry constraints, convexity, and geometric interpretation. The framework demonstrates strong performance in nonlinear scenarios like gamma correction and targeted value‑range filtering, achieving results comparable to state‑of‑the‑art PDE‑based methods.
By Freddie {\AA}str\"om, Michael Felsberg, George Baravdish
arXiv:2608.29172v1 Announce Type: new
Abstract: We present a novel variational approach to a tensor-based total variation formulation which is called gradient energy total variation, GETV. We introdu...
By Freddie {\AA}str\"om, George Baravdish, Michael Felsberg
arXiv:2609.01123v1 Announce Type: new
Abstract: Recent advancements in low-light image enhancement have leveraged diffusion models for their strong ability to generate perceptually realistic, detaile...
By Ruoyu Guo, Haonan Zhong, Maurice Pagnucco, Yang Song
arXiv:2606. 31061v1 Announce Type: cross Abstract: Tensor Train (TT) decomposition is a powerful technique for analyzing high-dimensional data.
By Hiroki Takeda, Yuto Miyatake, Daisuke Furihata
arXiv:2608.22302v1 Announce Type: new
Abstract: The case when a partial differential equation (PDE) can be considered as an Euler-Lagrange (E-L) equation of an energy functional, consisting of a data...
By Freddie {\AA}str\"om, George Baravdish, Michael Felsberg
Recent advancements in low-light image enhancement have leveraged diffusion models for their strong ability to generate perceptually realistic, detailed images. Patch diffusion models further offer a...
The paper proposes an implicit neural representation approach for compressing hyperspectral video, extending an existing RGB video compression model. It reports significant improvements, achieving +4.99 dB Bjørntegaard Delta PSNR and –88.88 % rate reduction versus frame‑by‑frame traditional methods. The method also boosts downstream object‑tracking performance, improving area‑under‑curve by up to 23.42 % and distance precision by up to 35.56 % on the HOT2026 dataset.
By Alfredo Scalera, Paul Murray, Jaime Zabalza
arXiv:2606. 04299v1 Announce Type: cross Abstract: We consider the problem of generating images whose internal structure -- defined by the distribution of patches across multiple scales -- matches that of a single reference image.
By Haojun Qiu, Kiriakos N. Kutulakos, David B. Lindell
arXiv:2609.12843v1 Announce Type: new
Abstract: Recently, tensor decompositions are prevalent for multi-dimensional image representation, which learn the instance-specific structure of each image fro...
By Bing-Zhang Fu, Zhi-Long Han, Ting-Zhu Huang, Xi-Le Zhao, Deyu Meng
arXiv:2606. 13580v1 Announce Type: cross Abstract: Event-based vision has drawn increasing attention owing to its distinctive properties, including ultra-high temporal resolution and extreme dynamic range.
By Dachun Kai, Jiayao Lu, Yueyi Zhang, Xiaoyan Sun
The paper introduces Quality-Constrained Image Coding for Machines (ICM), which compresses images by treating a computer vision application as the primary observer while limiting human-observed quality to a specified target. By formulating joint compression and segmentation as a constrained optimization problem, the authors design penalty functions that steer the codec toward the desired visual quality, allowing remaining coding capacity to enhance machine performance. Experiments demonstrate significant bitrate savings—up to 22.82% over unconstrained joint optimization and 29.81% over a simple rate–distortion baseline—while maintaining target visual quality without added complexity.
By Khoa Pham-Dinh, Sanaz Nami, Hamed Rezazadegan Tavakoli, Moncef Gabbouj, Farhad Pakdaman
arXiv:2605.11585v2 Announce Type: replace
Abstract: This paper addresses the problem of image denoising for grayscale images. We propose a probabilistic image generative model that combines a quadtre...
By Shota Saito, Yuta Nakahara, Kohei Horinouchi, Naoki Ichijo, Manabu Kobayashi, Toshiyasu Matsushima