arXiv Computer Vision

A Study of the Limits of Collaborative DCT-Based Image Denoising via Interpretable Neural Networks

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.

arXiv AI

Jul 31

PatchDenoiser: Parameter-efficient multi-scale patch learning and fusion denoiser for Low-dose CT imaging

arXiv:2602. 21987v3 Announce Type: replace-cross Abstract: Low-dose CT images are essential for reducing radiation exposure in cancer screening, pediatric imaging, and longitudinal monitoring protocols, but their quality is often degraded by noise from low-dose acquisition, patient motion, or scanner limitations, affecting both clinical interpretation and downstream analysis.

By Jitindra Fartiyal, Pedro Freire, Sergei K. Turitsyn, Sergei G. Solovski

llms diffusion fine-tuning benchmarks

arXiv Machine Learning

Sep 16

A deep dictionary network-based foundation model for ultra-low-dose CT denoising

The paper introduces a deep dictionary network (DDN) foundation model designed for ultra‑low‑dose CT (ULDCT) denoising across multiple organs. By cascading convolutional sparse coding layers with iterative soft‑thresholding, the architecture offers inherent interpretability, while dynamic dictionary and threshold modules enhance representation. The model is pre‑trained on over one million normal‑dose CT images and fine‑tuned on multi‑organ ULDCT datasets, achieving state‑of‑the‑art performance that consistently outperforms existing methods.

By Baoshun Shi, Shuangyi Yang, Ke Jiang, Bin Zhu, Zhanli Hu, Huazhu Fu

fine-tuning safety

arXiv Computer Vision

Sep 22

PixelDiT2: Representation-Grounded Pixel Diffusion Transformers

arXiv:2609.24919v1 Announce Type: new Abstract: Recent advances in pixel-space diffusion models have narrowed the image quality gap with latent-space diffusion, but still converge more slowly and lag...

By Yongsheng Yu, Wei Xiong, Yichen Sheng, Shiqiu Liu, Jiebo Luo

llms diffusion

arXiv AI

Jul 7

Pooling-Based Context Modeling for Convolution-Free Deep Image Prior

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

benchmarks

arXiv Computer Vision

Aug 25

CiUNet: A Hybrid Swin-CNN UNet for Medical Image Segmentation

arXiv:2608.22281v1 Announce Type: cross Abstract: Medical image segmentation requires high accuracy and robustness, yet practical commercial deployment also demands privacy preservation and computati...

By Bin Dong, Jinghong Chen

llms computer-vision benchmarks safety

arXiv Machine Learning

Jun 4

Efficient and Training-Free Single-Image Diffusion Models

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

diffusion benchmarks

arXiv AI

Sep 2

V-Co: A Closer Look at Visual Representation Alignment via Co-Denoising

V-Co investigates visual co-denoising for pixel-space diffusion models, using a unified JiT-based framework to isolate key design choices. The study identifies two essential components: a dual-stream architecture with flexible cross-stream interaction and a perceptual-drifting hybrid loss combined with RMS-based feature rescaling for stronger semantic supervision. Experiments on ImageNet-256 demonstrate that V-Co surpasses baseline pixel-space diffusion and strong prior pixel-diffusion methods at comparable model sizes while requiring fewer training epochs.

By Han Lin, Xichen Pan, Zun Wang, Yue Zhang, Chu Wang, Jaemin Cho, Mohit Bansal

diffusion safety

arXiv AI

Jul 24

From Attention to Frequency: Integration of Vision Transformer and FFT-ReLU for Enhanced Image Deblurring

arXiv:2511. 10806v1 Announce Type: cross Abstract: Image deblurring is vital in computer vision, aiming to recover sharp images from blurry ones caused by motion or camera shake.

By Syed Mumtahin Mahmud, Mahdi Mohd Hossain Noki, Prothito Shovon Majumder, Abdul Mohaimen Al Radi, Md. Haider Ali, Md. Mosaddek Khan

llms computer-vision efficiency benchmarks

Hugging Face Trending Papers

Jun 9

UniPET: a universal network for high-quality PET image denoising across varied dose reduction factors

Most existing deep learning-based PET image denoising methods assume a fixed and known dose reduction factor (DRF) for low-dose PET images. However, these methods encounter significant performance degradation when the DRF varies beyond the assumed one in practical applications.

benchmarks safety

arXiv AI

Jul 7

Steering Optimisation Trajectories in Diffusion Representation Learning

arXiv:2607. 05319v1 Announce Type: cross Abstract: We study why diffusion autoencoders can achieve similar image quality while learning substantially different latent structures.

By Rajat Rasal, Avinash Kori, Tian Xia, Ben Glocker

diffusion computer-vision benchmarks

arXiv Machine Learning

Jul 23

DREMnet: An Interpretable Denoising Framework for Semi-Airborne Transient Electromagnetic Signal

arXiv:2503. 22223v2 Announce Type: replace Abstract: The semi-airborne transient electromagnetic method (SATEM) is capable of conducting rapid surveys over large-scale and hard-to-reach areas.

By Shuang Wang, Ming Guo, Xuben Wang, Fei Deng, Lifeng Mao, Bin Wang, Wenlong Gao

llms rag safety

arXiv Computer Vision

Aug 25

Pixel-Space Diffusion via Observation Operators

Pixel‑Space Diffusion via Observation Operators introduces a new framework for pixel‑space diffusion models that addresses a scale‑time mismatch in existing methods. By replacing fixed full‑image supervision with a time‑indexed observation trajectory that progresses from coarse structures to the full image, the model aligns supervision with the natural recovery order of image details. The approach employs Gaussian‑Lanczos operators and a GL‑CoDA decoder to refine features progressively, resulting in faster convergence and higher generation quality, achieving an FID of 1.52 on ImageNet‑256.

By Shaojie Guo, Lichen Ma, Haoyang Tong, Yu He, Zipeng Guo, Xiaoan Liu, Feng Yan, Yu Guo, Fei Wang, Junshi Huang, Yan Wang

diffusion

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