DeepAI AI Chat
Log In Sign Up

Residual Local Feature Network for Efficient Super-Resolution

05/16/2022
by   Fangyuan Kong, et al.
ByteDance Inc.
0

Deep learning based approaches has achieved great performance in single image super-resolution (SISR). However, recent advances in efficient super-resolution focus on reducing the number of parameters and FLOPs, and they aggregate more powerful features by improving feature utilization through complex layer connection strategies. These structures may not be necessary to achieve higher running speed, which makes them difficult to be deployed to resource-constrained devices. In this work, we propose a novel Residual Local Feature Network (RLFN). The main idea is using three convolutional layers for residual local feature learning to simplify feature aggregation, which achieves a good trade-off between model performance and inference time. Moreover, we revisit the popular contrastive loss and observe that the selection of intermediate features of its feature extractor has great influence on the performance. Besides, we propose a novel multi-stage warm-start training strategy. In each stage, the pre-trained weights from previous stages are utilized to improve the model performance. Combined with the improved contrastive loss and training strategy, the proposed RLFN outperforms all the state-of-the-art efficient image SR models in terms of runtime while maintaining both PSNR and SSIM for SR. In addition, we won the first place in the runtime track of the NTIRE 2022 efficient super-resolution challenge. Code will be available at https://github.com/fyan111/RLFN.

READ FULL TEXT

page 3

page 4

page 5

04/18/2022

Fast and Memory-Efficient Network Towards Efficient Image Super-Resolution

Runtime and memory consumption are two important aspects for efficient i...
01/27/2022

Revisiting RCAN: Improved Training for Image Super-Resolution

Image super-resolution (SR) is a fast-moving field with novel architectu...
11/19/2016

Beyond Deep Residual Learning for Image Restoration: Persistent Homology-Guided Manifold Simplification

The latest deep learning approaches perform better than the state-of-the...
05/12/2022

Blueprint Separable Residual Network for Efficient Image Super-Resolution

Recent advances in single image super-resolution (SISR) have achieved ex...
12/16/2021

Feature Distillation Interaction Weighting Network for Lightweight Image Super-Resolution

Convolutional neural networks based single-image super-resolution (SISR)...
06/24/2018

CT-image Super Resolution Using 3D Convolutional Neural Network

Computed Tomography (CT) imaging technique is widely used in geological ...
11/13/2020

Lightweight Single-Image Super-Resolution Network with Attentive Auxiliary Feature Learning

Despite convolutional network-based methods have boosted the performance...