arXiv Machine Learning June 3, 2026 By Xiaolong Qian, Qi Jiang, Yao Gao, Lei Sun, Kailun Yang, Xian Wang, Zhonghua Yi, Wenyong Li, Ming-Hsuan Yang, Luc Van Gool, Kaiwei Wang

Towards Blind Lens Aberration Correction via Large LensLib Pre-training and Discrete Degradation Priors

Read the original on arXiv Machine Learning →

arXiv:2511. 17126v4 Announce Type: replace-cross Abstract: Emerging deep-learning-based lens library pre-training (LensLib-PT) pipeline offers a new avenue for blind lens aberration correction by training a universal neural network, demonstrating strong capability in handling diverse unknown optical degradations.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

Hugging Face Trending Papers

Jul 7

Realistic Compound-Lens Defocus Blur Synthesis

Defocus blur degrades fine image structures and limits visual perception, which can adversely affect downstream vision tasks. Although recent deep learning deblurring methods have achieved strong performance, their effectiveness depends on training data and often degrades across cameras and lenses due to limited optical diversity and realism in existing datasets.

safety

Hugging Face Trending Papers

Jul 13

FoundationGeo: Learning Spatial Pixel-Wise Fields for Monocular Metric Geometry

We present FoundationGeo, a two-stage framework that explicitly bridges relative and metric prediction via spatial calibration and principled data design. Stage 1 learns a high-fidelity, affine-invariant geometry model by initializing with DINOv3 and training on a curated 10.

benchmarks safety

arXiv AI

Aug 6

MODEST: Multi-Optics Depth-of-Field Stereo Dataset

arXiv:2511. 20853v4 Announce Type: replace-cross Abstract: Training and evaluation of state-of-the-art computer vision algorithms for reliable shallow depth of field (DoF) rendering and defocus deblurring remain constrained by a persistent lack of large-scale, full-frame, high fidelity, real-image datasets.

By Nisarg K. Trivedi, Vinayaka A. Belludi, Li-Yun Wang

computer-vision robotics benchmarks

arXiv AI

Jul 21

DepthART: Scaling Foundation Monocular Depth to Tiny Models

arXiv:2607. 17099v1 Announce Type: cross Abstract: Recent geometric foundation models (e.

By Feng Xue, Wu Chen, Mingshuai Zhao, Guofeng Zhong, Anlong Ming, Haozhe Wang, Dianqiao Lei, Zhaowen Lin, Haiyang Zhang, Nicu Sebe

computer-vision fine-tuning safety

arXiv Machine Learning

1d ago

Comprehensive language-image pre-training for 3D medical image understanding

arXiv:2510. 15042v3 Announce Type: replace-cross Abstract: In the 3D medical image domain, vision-language pre-training is used to create vision-language encoders (VLEs) that can support radiologists by retrieving patients with similar abnormalities, predicting likelihoods of abnormality, or, with downstream adaptation, generating radiological reports.

By Tassilo Wald, Ibrahim Ethem Hamamci, Yuan Gao, Sam Bond-Taylor, Harshita Sharma, Maximilian Ilse, Cynthia Lo, Olesya Melnichenko, Anton Schwaighofer, Noel C. F. Codella, Maria Teodora Wetscherek, Klaus H. Maier-Hein, Panagiotis Korfiatis, Valentina Salvatelli, Javier Alvarez-Valle, Fernando P\'erez-Garc\'ia

computer-vision multimodal benchmarks safety

arXiv Machine Learning

Jun 9

The Need for Neural ISP in the Small-Pixel Era: How Shrinking Pixels Push Optics to the Limit and Neural Restoration Pushes Back

arXiv:2606. 07675v1 Announce Type: cross Abstract: Smartphone telephoto cameras are approaching a "telephoto physics wall": as pixel pitches shrink toward sub-0.

By Jingxi Li, Neerja Aggarwal, Laurent Gudemann, Shivansh Rao, Vishal Vinod, Tom E. Bishop, Ziv Attar

Related stories