AI RESEARCH
Real Image Denoising with Knowledge Distillation for High-Performance Mobile NPUs
arXiv CS.LG
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ArXi:2605.03680v1 Announce Type: cross While deep-learning-based image restoration has achieved unprecedented fidelity, deployment on mobile Neural Processing Units (NPUs) remains bottlenecked by operator incompatibility and memory-access overhead. We propose an NPU-aware hardware-algorithm co-design approach for real-world image denoising on mobile NPUs. Our approach employs a high-capacity teacher to supervise a lightweight student network specifically designed to leverage the tiled-memory architectures of modern mobile SoCs.