Search Results for author:
Found 11 papers, 4 papers with code
Visible and infrared self-supervised fusion trained on a single example
For each such pair, RGB and NIR, the network is trained for seconds to deduce the final fusion.
Defect Detection Approaches Based on Simulated Reference Image
This work is addressing the problem of defect anomaly detection based on a clean reference image.
Automatic defect segmentation by unsupervised anomaly learning
We train a U-net shape network to segment defects using a dataset of clean background images.
Multispectral image fusion based on super pixel segmentation
This paper focuses on the task of fusing color (RGB) and near-infrared (NIR) images as this the typical RGBT sensors, as in multispectral cameras for detection, fusion, and dehazing.
Classic versus deep learning approaches to address computer vision challenges
Computer vision and image processing address many challenging applications.
Multi-scale Processing of Noisy Images using Edge Preservation Losses
As our experiments show, we achieve high-quality results in the three aspects of faint edge detection, noisy image classification and natural image denoising.
Deep Multi-Spectral Registration Using Invariant Descriptor Learning
Our algorithm detects corners by Harris and matches them by a patch-metric learned on top of CIFAR-10 network descriptor.
Registration and Fusion of Multi-Spectral Images Using a Novel Edge Descriptor
In this paper we introduce a fully end-to-end approach for multi-spectral image registration and fusion.
Photometric Stereo by Hemispherical Metric Embedding
We present a novel embedding method that maps pixels to normals on the unit hemisphere.
On Detection of Faint Edges in Noisy Images
A fundamental question for edge detection in noisy images is how faint can an edge be and still be detected.
Fast Detection of Curved Edges at Low SNR
Detecting edges is a fundamental problem in computer vision with many applications, some involving very noisy images.
