In this paper, we combine image denoising and classification, aiming to enhance human perception of noisy images captured by edge devices, like security cameras. Since edge devices have little computational power, we also optimize for efficiency by proposing a novel architecture that integrates the two tasks. Additionally, we alter a Neural Architecture Search (NAS) method, which searches for classifiers, to search for the integrated model while optimizing for a target latency, classification accuracy, and denoising performance. Our NAS architectures outperform our manually designed alternatives in both denoising and classification, offering a significant improvement to human perception. Moreover, our approach empowers users to construct architectures tailored to domains like medical imaging, surveillance systems, and industrial inspections.

Proper maintenance and inspection of aircraft and their engines is important for society. These engine inspections are performed using borescopes of which the footage is manually analysed. Having the opportunity to reconstruct a 3D model of the rotors would ease the inspection and introduce the possibility to automate the process. Monocular SLAM systems are capable of reconstructing such models in real-time using a video of the rotors. However, SLAM is not tested in environments similar to the aircraft turbine. This study, therefore, assesses the performance of different SLAM approaches in this specific setting. The results show that 3D reconstruction of aircraft engines using direct SLAM has potential for damage assessment. Further research into damage assessment using SLAM is therefore viable.