Conv-Former: A Novel Network Combining Convolution and Self-Attention for Image Quality Assessment

doi:10.3390/s23010427

CORC > 长春光学精密机械与物理研究所 > 中国科学院长春光学精密机械与物理研究所

	Conv-Former: A Novel Network Combining Convolution and Self-Attention for Image Quality Assessment
	L. Han; H. Lv; Y. Zhao; H. Liu; G. Bi; Z. Yin and Y. Fang
刊名	Sensors
	2023
卷号	23 期号:1
ISSN号	14248220
DOI	10.3390/s23010427
英文摘要	To address the challenge of no-reference image quality assessment (NR-IQA) for authentically and synthetically distorted images, we propose a novel network called the Combining Convolution and Self-Attention for Image Quality Assessment network (Conv-Former). Our model uses a multi-stage transformer architecture similar to that of ResNet-50 to represent appropriate perceptual mechanisms in image quality assessment (IQA) to build an accurate IQA model. We employ adaptive learnable position embedding to handle images with arbitrary resolution. We propose a new transformer block (TB) by taking advantage of transformers to capture long-range dependencies, and of local information perception (LIP) to model local features for enhanced representation learning. The module increases the model’s understanding of the image content. Dual path pooling (DPP) is used to keep more contextual image quality information in feature downsampling. Experimental results verify that Conv-Former not only outperforms the state-of-the-art methods on authentic image databases, but also achieves competing performances on synthetic image databases which demonstrate the strong fitting performance and generalization capability of our proposed model. © 2022 by the authors.
URL标识	查看原文
内容类型	期刊论文
源URL	[http://ir.ciomp.ac.cn/handle/181722/67513]
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	L. Han,H. Lv,Y. Zhao,et al. Conv-Former: A Novel Network Combining Convolution and Self-Attention for Image Quality Assessment[J]. Sensors,2023,23(1).
APA	L. Han,H. Lv,Y. Zhao,H. Liu,G. Bi,&Z. Yin and Y. Fang.(2023).Conv-Former: A Novel Network Combining Convolution and Self-Attention for Image Quality Assessment.Sensors,23(1).
MLA	L. Han,et al."Conv-Former: A Novel Network Combining Convolution and Self-Attention for Image Quality Assessment".Sensors 23.1(2023).