.png){kind=icon}
返回
RefQSR: Reference-Based Quantization for Image Super-Resolution Networks
.png){kind=icon}
OA
AI
DOI：10.1109/TIP.2024.3385276.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Single image super-resolution (SISR) aims to reconstruct a high-resolution image from its low-resolution observation. Recent deep learning-based SISR models show high performance at the expense of increased computational costs, limiting their use in resource-constrained environments. As a promising solution for computationally efficient network design, network quantization has been extensively studied. However, existing quantization methods developed for SISR have yet to effectively exploit image self-similarity, which is a new direction for exploration in this study. We introduce a novel method called reference-based quantization for image super-resolution (RefQSR) that applies high-bit quantization to several representative patches and uses them as references for low-bit quantization of the rest of the patches in an image. To this end, we design dedicated patch clustering and reference-based quantization modules and integrate them into existing SISR network quantization methods. The experimental results demonstrate the effectiveness of RefQSR on various SISR networks and quantization methods.
Keyword:
Quantization (signal)
Superresolution
Computational efficiency
Task analysis
Image reconstruction
Upper bound
Limiting
Deep learning
image super-resolution
network quantization
reference-based quantization
期刊
IF:
13.7
论文数:
1.0W
被引数:
8.4W
机构
引用论文
.png){kind=icon}
Kitsune: An Ensemble of Autoencoders for Online Network Intrusion DetectionKitsune: 用于在线网络入侵检测的自动编码器的集合OAAI
OAAI分享收藏
查看更多内容