Wenbo Zhang, Lulu Pan, Ke Xu, Guo Li, Yanheng Lv, Lingxiao Li, Le Lei
{"title":"自适应可学习的图像细节增强网络","authors":"Wenbo Zhang, Lulu Pan, Ke Xu, Guo Li, Yanheng Lv, Lingxiao Li, Le Lei","doi":"10.1016/j.image.2025.117319","DOIUrl":null,"url":null,"abstract":"<div><div>In recent years, single image super-resolution (SISR) methods based on deep learning have advanced significantly. However, their high computational complexity and memory demands hinder deployment on resource-constrained devices. Although numerous lightweight super-resolution (SR) methods have been proposed to address this issue, most fail to distinguish between flat and detailed regions in images, treating them uniformly. This lack of targeted design for detailed regions, which are critical to SR performance, results in redundancy and inefficiency in existing lightweight methods. To address these challenges, we propose a simple yet effective network Self-adaptive and Learnable Detail Enhancement Network (LDEN) that specifically focuses on the reconstruction of detailed regions. Firstly, we present two designs for the reconstruction of detailed regions: (1) we design the Learnable Detail Extraction Block (LDEB), which can pay special attention to detailed regions and employ a larger convolution kernel in LDEB to obtain a larger receptive field; (2) we design a lightweight attention mechanism called Detail-oriented Spatial Attention (DSA) to enhance the network's ability to reconstruct detailed regions. Secondly, we design a hierarchical refinement mechanism named Efficient Hierarchical Refinement Block (EHRB) which can reduce the inadequate information extraction and integration caused by rough single-layer refinement. Extensive experiments demonstrate that LDEN achieves state-of-the-art performance on all benchmark datasets. Notably, for 4 × magnification tasks, LDEN outperforms BSRN - the champion of the model complexity track of NTIRE 2022 Efficient SR Challenge - by achieving gains of 0.11 dB and 0.12 dB while reducing parameters by nearly 10 %.</div></div>","PeriodicalId":49521,"journal":{"name":"Signal Processing-Image Communication","volume":"137 ","pages":"Article 117319"},"PeriodicalIF":3.4000,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Self-adaptive and learnable detail enhancement network for efficient image super resolution\",\"authors\":\"Wenbo Zhang, Lulu Pan, Ke Xu, Guo Li, Yanheng Lv, Lingxiao Li, Le Lei\",\"doi\":\"10.1016/j.image.2025.117319\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>In recent years, single image super-resolution (SISR) methods based on deep learning have advanced significantly. However, their high computational complexity and memory demands hinder deployment on resource-constrained devices. Although numerous lightweight super-resolution (SR) methods have been proposed to address this issue, most fail to distinguish between flat and detailed regions in images, treating them uniformly. This lack of targeted design for detailed regions, which are critical to SR performance, results in redundancy and inefficiency in existing lightweight methods. To address these challenges, we propose a simple yet effective network Self-adaptive and Learnable Detail Enhancement Network (LDEN) that specifically focuses on the reconstruction of detailed regions. Firstly, we present two designs for the reconstruction of detailed regions: (1) we design the Learnable Detail Extraction Block (LDEB), which can pay special attention to detailed regions and employ a larger convolution kernel in LDEB to obtain a larger receptive field; (2) we design a lightweight attention mechanism called Detail-oriented Spatial Attention (DSA) to enhance the network's ability to reconstruct detailed regions. Secondly, we design a hierarchical refinement mechanism named Efficient Hierarchical Refinement Block (EHRB) which can reduce the inadequate information extraction and integration caused by rough single-layer refinement. Extensive experiments demonstrate that LDEN achieves state-of-the-art performance on all benchmark datasets. Notably, for 4 × magnification tasks, LDEN outperforms BSRN - the champion of the model complexity track of NTIRE 2022 Efficient SR Challenge - by achieving gains of 0.11 dB and 0.12 dB while reducing parameters by nearly 10 %.</div></div>\",\"PeriodicalId\":49521,\"journal\":{\"name\":\"Signal Processing-Image Communication\",\"volume\":\"137 \",\"pages\":\"Article 117319\"},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2025-04-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Signal Processing-Image Communication\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0923596525000669\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Signal Processing-Image Communication","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0923596525000669","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Self-adaptive and learnable detail enhancement network for efficient image super resolution
In recent years, single image super-resolution (SISR) methods based on deep learning have advanced significantly. However, their high computational complexity and memory demands hinder deployment on resource-constrained devices. Although numerous lightweight super-resolution (SR) methods have been proposed to address this issue, most fail to distinguish between flat and detailed regions in images, treating them uniformly. This lack of targeted design for detailed regions, which are critical to SR performance, results in redundancy and inefficiency in existing lightweight methods. To address these challenges, we propose a simple yet effective network Self-adaptive and Learnable Detail Enhancement Network (LDEN) that specifically focuses on the reconstruction of detailed regions. Firstly, we present two designs for the reconstruction of detailed regions: (1) we design the Learnable Detail Extraction Block (LDEB), which can pay special attention to detailed regions and employ a larger convolution kernel in LDEB to obtain a larger receptive field; (2) we design a lightweight attention mechanism called Detail-oriented Spatial Attention (DSA) to enhance the network's ability to reconstruct detailed regions. Secondly, we design a hierarchical refinement mechanism named Efficient Hierarchical Refinement Block (EHRB) which can reduce the inadequate information extraction and integration caused by rough single-layer refinement. Extensive experiments demonstrate that LDEN achieves state-of-the-art performance on all benchmark datasets. Notably, for 4 × magnification tasks, LDEN outperforms BSRN - the champion of the model complexity track of NTIRE 2022 Efficient SR Challenge - by achieving gains of 0.11 dB and 0.12 dB while reducing parameters by nearly 10 %.
期刊介绍:
Signal Processing: Image Communication is an international journal for the development of the theory and practice of image communication. Its primary objectives are the following:
To present a forum for the advancement of theory and practice of image communication.
To stimulate cross-fertilization between areas similar in nature which have traditionally been separated, for example, various aspects of visual communications and information systems.
To contribute to a rapid information exchange between the industrial and academic environments.
The editorial policy and the technical content of the journal are the responsibility of the Editor-in-Chief, the Area Editors and the Advisory Editors. The Journal is self-supporting from subscription income and contains a minimum amount of advertisements. Advertisements are subject to the prior approval of the Editor-in-Chief. The journal welcomes contributions from every country in the world.
Signal Processing: Image Communication publishes articles relating to aspects of the design, implementation and use of image communication systems. The journal features original research work, tutorial and review articles, and accounts of practical developments.
Subjects of interest include image/video coding, 3D video representations and compression, 3D graphics and animation compression, HDTV and 3DTV systems, video adaptation, video over IP, peer-to-peer video networking, interactive visual communication, multi-user video conferencing, wireless video broadcasting and communication, visual surveillance, 2D and 3D image/video quality measures, pre/post processing, video restoration and super-resolution, multi-camera video analysis, motion analysis, content-based image/video indexing and retrieval, face and gesture processing, video synthesis, 2D and 3D image/video acquisition and display technologies, architectures for image/video processing and communication.