7 citations · 7 across the 5 of their papers we have counts for
6 papers
AIM 2025 Challenge on Real-World RAW Image Denoising
Feiran Li, Jiacheng Li, Marcos V. Conde +4
We introduce the AIM 2025 Real-World RAW Image Denoising Challenge, aiming to advance efficient and effective denoising techniques grounded in data synthesis. The competition is bu…
RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report
Marcos V. Conde, Radu Timofte, Radu Berdan +30
Numerous low-level vision tasks operate in the RAW domain due to its linear properties, bit depth, and sensor designs. Despite this, RAW image datasets are scarce and more expensiv…
ReRAW: RGB-to-RAW Image Reconstruction via Stratified Sampling for Efficient Object Detection on the Edge
Radu Berdan, Beril Besbinar, Christoph Reinders +2
Edge-based computer vision models running on compact, resource-limited devices benefit greatly from using unprocessed, detail-rich RAW sensor data instead of processed RGB images.…
RAW-Diffusion: RGB-Guided Diffusion Models for High-Fidelity RAW Image Generation
Christoph Reinders, Radu Berdan, Beril Besbinar +2
Current deep learning approaches in computer vision primarily focus on RGB data sacrificing information. In contrast, RAW images offer richer representation, which is crucial for p…
A Structured Dictionary Perspective on Implicit Neural Representations
Gizem Yüce, Guillermo Ortiz-Jiménez, Beril Besbinar +1
Implicit neural representations (INRs) have recently emerged as a promising alternative to classical discretized representations of signals. Nevertheless, despite their practical s…
Self-Supervision by Prediction for Object Discovery in Videos
Beril Besbinar, Pascal Frossard
Despite their irresistible success, deep learning algorithms still heavily rely on annotated data. On the other hand, unsupervised settings pose many challenges, especially about d…