3 research outputs found
MFDNet: Towards Real-time Image Denoising On Mobile Devices
Deep convolutional neural networks have achieved great progress in image
denoising tasks. However, their complicated architectures and heavy
computational cost hinder their deployments on a mobile device. 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. By performing
extensive analysis and experiments, we identify the network architectures that
can fully utilize powerful neural processing units (NPUs) and thus 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.Comment: Under review at the 2023 IEEE International Conference on Acoustics,
Speech, and Signal Processing (ICASSP 2023
Progressive Training of A Two-Stage Framework for Video Restoration
As a widely studied task, video restoration aims to enhance the quality of
the videos with multiple potential degradations, such as noises, blurs and
compression artifacts. Among video restorations, compressed video quality
enhancement and video super-resolution are two of the main tacks with
significant values in practical scenarios. Recently, recurrent neural networks
and transformers attract increasing research interests in this field, due to
their impressive capability in sequence-to-sequence modeling. However, the
training of these models is not only costly but also relatively hard to
converge, with gradient exploding and vanishing problems. To cope with these
problems, we proposed a two-stage framework including a multi-frame recurrent
network and a single-frame transformer. Besides, multiple training strategies,
such as transfer learning and progressive training, are developed to shorten
the training time and improve the model performance. Benefiting from the above
technical contributions, our solution wins two champions and a runner-up in the
NTIRE 2022 super-resolution and quality enhancement of compressed video
challenges.Comment: Winning two championships and one runner-up in the NTIRE 2022
challenge of super-resolution and quality enhancement of compressed video;
accepted to CVPRW 202