A 1.96M-parameter LiteDenoiseNet student model achieves 37.58 dB PSNR on full-resolution real image denoising benchmarks while running in 34-46 ms on mobile NPUs by leveraging NPU-compatible primitives and high-alpha knowledge distillation.
Lightweight modules for ef- ficient deep learning based image restoration.IEEE Trans- actions on Circuits and Systems for Video Technology, 31(3): 1168–1180
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Real Image Denoising with Knowledge Distillation for High-Performance Mobile NPUs
A 1.96M-parameter LiteDenoiseNet student model achieves 37.58 dB PSNR on full-resolution real image denoising benchmarks while running in 34-46 ms on mobile NPUs by leveraging NPU-compatible primitives and high-alpha knowledge distillation.