论文标题
轻量级网络朝着移动设备上的实时图像降级
Lightweight network towards real-time image denoising on mobile devices
论文作者
论文摘要
深度卷积神经网络在图像DeNo的任务方面取得了巨大进展。但是，它们复杂的体系结构和繁重的计算成本阻碍了他们在移动设备上的部署。最新设计轻巧的denoising网络的努力集中在减少拖鞋（浮点操作）或参数数量。但是，这些指标与设备潜伏期没有直接相关。在本文中，我们确定了影响基于CNN的模型在移动设备上的运行时性能的实际瓶颈：内存访问成本和与NPU不合时宜的操作，并基于这些操作构建模型。为了进一步提高降级性能，提出了对移动友好的关注模块MFA和模型重新聚集模块Repconv，它们既享有低潜伏期又具有出色的脱氧性能。为此，我们提出了一个对移动设备友好的Denoising网络，即MFDNET。实验表明，MFDNET在移动设备上的实时延迟下实现了现实世界中的基准SIDD和DND的最新性能。代码和预培训模型将发布。
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.
