arXiv:2607. 02952v1 Announce Type: cross Abstract: Convolutional Neural Networks (CNNs) achieve strong denoising performance by exploiting spatial context from neighboring pixels.
By Gihyun Kim, Jong-Seok Lee
arXiv:2507.08375v2 Announce Type: replace
Abstract: Video restoration and enhancement are critical not only for improving visual quality, but also as essential pre-processing steps to boost the perfo...
By Alexandra Malyugina, Yini Li, Joanne Lin, Nantheera Anantrasirichai
arXiv:2608.29038v1 Announce Type: new
Abstract: Noise poses a widespread challenge in signal processing, particularly when it comes to denoising images. Although convolutional neural networks (CNNs)...
By Dongjin Kim, Donggoo Jung, Sungyong Baik, Tae Hyun Kim
arXiv:2504.10201v3 Announce Type: replace
Abstract: In this paper, we introduce a synthetic image generator relying on a few simple principles, specifically focusing on geometric modeling, textures,...
By Raphael Achddou, Yann Gousseau, Sa\"id Ladjal, Sabine S\"usstrunk
The paper introduces DeepBM3D, a fully differentiable neural network that emulates the collaborative filtering strategy of BM3D for image denoising. It integrates non‑local patch grouping, DCT‑domain filtering with learned Wiener weights, and multi‑stage refinement, guided by lightweight convolutional feature extractors. Experiments demonstrate that DeepBM3D outperforms classical and hybrid baselines, competes with FFDNet at low to moderate noise levels, and excels on images with repetitive textures.
By Cristian Comellas, Julia Navarro, Antoni Buades
arXiv:2307. 00919v2 Announce Type: replace-cross Abstract: Over the past decade deep learning has revolutionized the field of computer vision, with convolutional neural network models proving to be very effective for image classification benchmarks.
By Vinoth Nandakumar, Arush Tagade, Tongliang Liu