  Deep convolutional neural networks have achieved great progress in image
denoising tasks. However, their complicated architectures and heavy
computational cost hinder their deployments on mobile devices. Some recent
efforts in designing lightweight denoising networks focus on reducing either
FLOPs (floating-point operations) or the number of parameters. However, these
metrics are not directly correlated with the on-device latency. In this paper,
we identify the real bottlenecks that affect the CNN-based models' run-time
performance on mobile devices: memory access cost and NPU-incompatible
operations, and build the model based on these. To further improve the
denoising performance, the mobile-friendly attention module MFA and the model
reparameterization module RepConv are proposed, which enjoy both low latency
and excellent denoising performance. To this end, we propose a mobile-friendly
denoising network, namely MFDNet. The experiments show that MFDNet achieves
state-of-the-art performance on real-world denoising benchmarks SIDD and DND
under real-time latency on mobile devices. The code and pre-trained models will
be released.
