arXiv AI
Jul 29
Image Quality Dependent Degradation for AI Systems
arXiv:2607. 25736v1 Announce Type: cross Abstract: Perception is one of the primary applications where neural networks outperform conventional algorithms.
Filtering-out poor-quality images for data preparation
Filtering noise is a fundamental part of data preparation that enhances image quality for applications such as object segmentation, detection, and recognition. Various noise reduction techniques are proposed in the literature, including the use of median, Gaussian, and bilateral filters.
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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.
arXiv AI
Jul 28
Multi-model approach for autonomous driving: A comprehensive study on traffic sign-, vehicle- and lane detection and behavioral cloning
arXiv:2603. 09255v2 Announce Type: replace-cross Abstract: Deep learning and computer vision techniques have become increasingly important in the development of self-driving cars.
arXiv AI
Jul 20
Why do CNNs excel at feature extraction? A mathematical explanation
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.
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.
arXiv Machine Learning
Jun 9
Balancing Real and Synthetic Data for CNN-based Masonry Crack Detection
arXiv:2606. 08033v1 Announce Type: cross Abstract: Cracks are a critical indicator of building health, and early stage identification is fundamental to prevent harmful damages.
arXiv AI
Jun 16
Revealing Artifacts via Noise Amplification: A Novel Perspective for AI-Generated Video Detection
arXiv:2606. 16742v1 Announce Type: cross Abstract: With the rapid advancement of video generation models, distinguishing between AI-generated and authentic videos has emerged as a challenging endeavor.
arXiv AI
Jun 11
SDQM: Synthetic Data Quality Metric for Object Detection Dataset Evaluation
arXiv:2510. 06596v2 Announce Type: replace-cross Abstract: The performance of machine learning models depends heavily on training data.
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.
Hugging Face Trending Papers
Jun 23
Flood Mapping from RGB imagery using a Vision Foundation Model
Timely, high-resolution maps of flood extent around settlements are essential for emergency response and damage assessment. We consider airborne RGB imagery for flood mapping as it can be collected rapidly at low cost.
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.
arXiv Machine Learning
Jun 18
Investigation of Neural Network Methods for Reconstruction and Classification of Texture Images Under Conditions of Incomplete Information
arXiv:2204. 14224v3 Announce Type: replace-cross Abstract: The automated analysis of heterogeneous natural textures is frequently hindered by physical damage and data loss, presenting a significant challenge to computer vision.