Residual Local Feature Network for Efficient Super-Resolution
- URL: http://arxiv.org/abs/2205.07514v1
- Date: Mon, 16 May 2022 08:46:34 GMT
- Title: Residual Local Feature Network for Efficient Super-Resolution
- Authors: Fangyuan Kong, Mingxi Li, Songwei Liu, Ding Liu, Jingwen He, Yang Bai,
Fangmin Chen, Lean Fu
- Abstract summary: 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.
In addition, we won the first place in the runtime track of the NTIRE 2022 efficient super-resolution challenge.
- Score: 20.62809970985125
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
Related papers
- HASN: Hybrid Attention Separable Network for Efficient Image Super-resolution [5.110892180215454]
lightweight methods for single image super-resolution achieved impressive performance due to limited hardware resources.
We find that using residual connections after each block increases the model's storage and computational cost.
We use depthwise separable convolutions, fully connected layers, and activation functions as the basic feature extraction modules.
arXiv Detail & Related papers (2024-10-13T14:00:21Z) - LeRF: Learning Resampling Function for Adaptive and Efficient Image Interpolation [64.34935748707673]
Recent deep neural networks (DNNs) have made impressive progress in performance by introducing learned data priors.
We propose a novel method of Learning Resampling (termed LeRF) which takes advantage of both the structural priors learned by DNNs and the locally continuous assumption.
LeRF assigns spatially varying resampling functions to input image pixels and learns to predict the shapes of these resampling functions with a neural network.
arXiv Detail & Related papers (2024-07-13T16:09:45Z) - Transforming Image Super-Resolution: A ConvFormer-based Efficient Approach [58.57026686186709]
We introduce the Convolutional Transformer layer (ConvFormer) and propose a ConvFormer-based Super-Resolution network (CFSR)
CFSR inherits the advantages of both convolution-based and transformer-based approaches.
Experiments demonstrate that CFSR strikes an optimal balance between computational cost and performance.
arXiv Detail & Related papers (2024-01-11T03:08:00Z) - Iterative Soft Shrinkage Learning for Efficient Image Super-Resolution [91.3781512926942]
Image super-resolution (SR) has witnessed extensive neural network designs from CNN to transformer architectures.
This work investigates the potential of network pruning for super-resolution iteration to take advantage of off-the-shelf network designs and reduce the underlying computational overhead.
We propose a novel Iterative Soft Shrinkage-Percentage (ISS-P) method by optimizing the sparse structure of a randomly network at each and tweaking unimportant weights with a small amount proportional to the magnitude scale on-the-fly.
arXiv Detail & Related papers (2023-03-16T21:06:13Z) - RDRN: Recursively Defined Residual Network for Image Super-Resolution [58.64907136562178]
Deep convolutional neural networks (CNNs) have obtained remarkable performance in single image super-resolution.
We propose a novel network architecture which utilizes attention blocks efficiently.
arXiv Detail & Related papers (2022-11-17T11:06:29Z) - Image Superresolution using Scale-Recurrent Dense Network [30.75380029218373]
Recent advances in the design of convolutional neural network (CNN) have yielded significant improvements in the performance of image super-resolution (SR)
We propose a scale recurrent SR architecture built upon units containing series of dense connections within a residual block (Residual Dense Blocks (RDBs))
Our scale recurrent design delivers competitive performance for higher scale factors while being parametrically more efficient as compared to current state-of-the-art approaches.
arXiv Detail & Related papers (2022-01-28T09:18:43Z) - Fourier Space Losses for Efficient Perceptual Image Super-Resolution [131.50099891772598]
We show that it is possible to improve the performance of a recently introduced efficient generator architecture solely with the application of our proposed loss functions.
We show that our losses' direct emphasis on the frequencies in Fourier-space significantly boosts the perceptual image quality.
The trained generator achieves comparable results with and is 2.4x and 48x faster than state-of-the-art perceptual SR methods RankSRGAN and SRFlow respectively.
arXiv Detail & Related papers (2021-06-01T20:34:52Z) - Hierarchical Residual Attention Network for Single Image
Super-Resolution [2.0571256241341924]
This paper introduces a new lightweight super-resolution model based on an efficient method for residual feature and attention aggregation.
Our proposed architecture surpasses state-of-the-art performance in several datasets, while maintaining relatively low computation and memory footprint.
arXiv Detail & Related papers (2020-12-08T17:24:28Z) - Iterative Network for Image Super-Resolution [69.07361550998318]
Single image super-resolution (SISR) has been greatly revitalized by the recent development of convolutional neural networks (CNN)
This paper provides a new insight on conventional SISR algorithm, and proposes a substantially different approach relying on the iterative optimization.
A novel iterative super-resolution network (ISRN) is proposed on top of the iterative optimization.
arXiv Detail & Related papers (2020-05-20T11:11:47Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.