(untitled) (Journal of Problems in Computer Science and Information Technologies, 2026)
Low-light perception is a core requirement for robotics, inspection, and wearable situational awareness, yet reliable night- vision solutions often require expensive sensors or computation- ally heavy processing. This manuscript presents (i) a low-cost near- infrared (NIR) night-vision camera system with real-time enhancement and mixed-reality visualization, and (ii) a controlled benchmarking protocol that compares multiple sensor modalities and classical en- hancement pipelines on a Linux embedded platform. To prioritize real-time feasibility, we deliberately avoid neural networks and other AI/ML models, and instead benchmark lightweight classical methods: CLAHE, bilateral filtering + CLAHE, Non-Local Means (NLM) + CLAHE, Retinex SSR, Retinex SSR with percentile normalization, and two proposed pipelines (a CLAHE-based pipeline and a Retinex integrated variant). Experiments were conducted in a fully dark room using identical illumination conditions and multiple observation distances. We report runtime (ms/frame, FPS) and no- reference quality metrics (entropy, edge strength, Laplacian variance, RMS contrast, mean intensity) to accommodate the absence of ground-truth references. A representative run shows that the proposed CLAHE-based pipeline achieves 246.5 FPS at 640×480 while substantially improving objective no-reference metrics relative to raw frames, whereas Retinex with percentile normalization yields higher contrast/entropy but at 12.7 FPS. Hardware benchmarking indicates that thermal imaging provides the clearest visibility but at high cost/power, Kinect requires stronger illumination, and event-based sensing offers extremely low latency but requires temporal modulation to observe static targets.