Mohamed Youssef

Mohamed Youssef, Oliver Bimber

Occlusion-robust scene recovery remains a major challenge in computational imaging, particularly in natural environments where dense foreground vegetation severely limits visibility. We propose a vision-reasoning-guided light field occlusion removal framework that combines the visibility recovery capability of light field integration (LFI) with the semantic reasoning capacity of vision-language models (VLMs). Multi-view observations are first integrated via LFI to suppress foreground occlusions and produce an initial visibility-enhanced representation. A VLM is then incorporated as a conditional semantic prior to restore degraded structures and recover fine details, guided by the observed measurements. To improve recovery consistency and reduce hallucination artifacts, we introduce a multi-sample fusion strategy that aggregates multiple generated hypotheses into a unified estimate. Experimental results on synthetic and real-world datasets demonstrate state-of-the-art performance, achieving the highest average SSIM across four synthetic light field benchmark scenes (4-Syn) and strong generalization across structured and unstructured acquisition settings. These results highlight the effectiveness of combining physical imaging constraints with vision-language reasoning for robust perception under severe occlusion, with applicability to search-and-rescue and exploratory robotic navigation.

Mohamed Youssef, Lukas Brunner, Klaus Rundhammer et al.

We introduce a novel method for reconstructing surface temperatures through occluding forest vegetation by combining signal processing and machine learning. Our goal is to enable fully automated aerial wildfire monitoring using autonomous drones, allowing for the early detection of ground fires before smoke or flames are visible. While synthetic aperture (SA) sensing mitigates occlusion from the canopy and sunlight, it introduces thermal blur that obscures the actual surface temperatures. To address this, we train a visual state space model to recover the subtle thermal signals of partially occluded soil and fire hotspots from this blurred data. A key challenge was the scarcity of real-world training data. We overcome this by integrating a latent diffusion model into a vector quantized to generated a large volume of realistic surface temperature simulations from real wildfire recordings, which we further expanded through temperature augmentation and procedural thermal forest simulation. On simulated data across varied ambient and surface temperatures, forest densities, and sunlight conditions, our method reduced the RMSE by a factor of 2 to 2.5 compared to conventional thermal and uncorrected SA imaging. In field experiments focused on high-temperature hotspots, the improvement was even more significant, with a 12.8-fold RMSE gain over conventional thermal and a 2.6-fold gain over uncorrected SA images. We also demonstrate our model's generalization to other thermal signals, such as human signatures for search and rescue. Since simple thresholding is frequently inadequate for detecting subtle thermal signals, the morphological characteristics are equally essential for accurate classification. Our experiments demonstrated another clear advantage: we reconstructed the complete morphology of fire and human signatures, whereas conventional imaging is defeated by partial occlusion.