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Marius Pedersen

Professor

Norwegian University of Science and Technology

Norway

Research Interests

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About

Dr. Marius Pedersen is an Associate Professor at NTNU in Norway. His research areas include computer vision, medical imaging, and deep learning applications, as evidenced by his recent articles on topics such as underwater video monitoring, medical image enhancement, and fairness in deepfake detection. He is also involved in innovative techniques for analyzing visual data, including techniques for temporal knowledge learning and capsule endoscope video analysis.

Articles (18)

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2027

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2026

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2026

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2026

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2026

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2022

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2022

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2020

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2020

Collaborators (4)

Sophie Triantaphillidou

Professor

University of Westminster

UNITED KINGDOM

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Hao Wang

Associate Professor

Norwegian University of Science and Technology

NORWAY

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Seyed Ali Amirshahi

Associate Professor

Norwegian University of Science and Technology

NORWAY

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Azeddine Beghdadi

Professor

Sorbonne Université

FRANCE

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Marius Pedersen

Research Interests

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About

Articles (18)

Few-Shot Supervised Contrastive Learning for Image/Video Distortion Classification

Uncertainty Quantification in Video Distortion Classification Under Dataset Shift

Optimizing Pseudo‐Isochromatic Plates Based Testing Through Adaptive Spatial and Chromatic Modulation

Predicting image quality score distribution using deep neural networks

D 2FNet: Degradation-Aware Diffusion Feature-Guided Network Against Physical-World Attack for Monocular Depth Estimation

Behavior of Image Information Metrics Under Controlled Blur and Noise Distortions

Monitoring Fish Assemblages in Seasonal Off‐Channel Habitats Using Underwater Video and Computer Vision

3D Reconstruction of the Human Colon From Capsule Endoscope Video

Individual Contrast Preferences in Natural Images

Analyzing Fairness in Deepfake Detection With Massively Annotated Databases

Terrain-Informed Self-Supervised Learning: Enhancing Building Footprint Extraction From LiDAR Data With Limited Annotations

SSP-Net: A Siamese-based Structure-Preserving Generative Adversarial Network for Unpaired Medical Image Enhancement

FCFusion: Fractal Componentwise Modeling With Group Sparsity for Medical Image Fusion

What Elevation Makes 2.5D Prints Perceptually Natural?

Learning the Distribution-Based Temporal Knowledge With Low Rank Response Reasoning for UAV Visual Tracking

Multichannel Residual Cues for Fine-Grained Classification in Wireless Capsule Endoscopy

A Critical Analysis on Perceptual Contrast and Its Use in Visual Information Analysis and Processing

Weakly-Supervised Network for Detection of COVID-19 in Chest CT Scans

Collaborators (4)

Sophie Triantaphillidou

Hao Wang

Seyed Ali Amirshahi

Azeddine Beghdadi

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