Joint Luminance-Saliency Prior and Attention for Underwater Image Quality Assessment
Guardado en:
| Publicado en: | Remote Sensing vol. 16, no. 16 (2024), p. 3021 |
|---|---|
| Autor principal: | |
| Otros Autores: | , , , |
| Publicado: |
MDPI AG
|
| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| Etiquetas: |
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3098193888 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2072-4292 | ||
| 024 | 7 | |a 10.3390/rs16163021 |2 doi | |
| 035 | |a 3098193888 | ||
| 045 | 2 | |b d20240101 |b d20241231 | |
| 084 | |a 231556 |2 nlm | ||
| 100 | 1 | |a Lin, Zhiqiang |u College of Science and Technology, Ningbo University, Ningbo 315212, China; <email>2211170008@nbu.edu.cn</email> (Z.L.); <email>jinchongchong@nbu.edu.cn</email> (C.J.); <email>luoting@nbu.edu.cn</email> (T.L.) | |
| 245 | 1 | |a Joint Luminance-Saliency Prior and Attention for Underwater Image Quality Assessment | |
| 260 | |b MDPI AG |c 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Underwater images, as a crucial medium for storing ocean information in underwater sensors, play a vital role in various underwater tasks. However, they are prone to distortion due to the imaging environment, which leads to a decline in visual quality, which is an urgent issue for various marine vision systems to address. Therefore, it is necessary to develop underwater image enhancement (UIE) and corresponding quality assessment methods. At present, most underwater image quality assessment (UIQA) methods primarily rely on extracting handcrafted features that characterize degradation attributes, which struggle to measure complex mixed distortions and often exhibit discrepancies with human visual perception in practical applications. Furthermore, current UIQA methods lack the consideration of the perception perspective of enhanced effects. To this end, this paper employs luminance and saliency priors as critical visual information for the first time to measure the enhancement effect of global and local quality achieved by the UIE algorithms, named JLSAU. The proposed JLSAU is built upon an overall pyramid-structured backbone, supplemented by the Luminance Feature Extraction Module (LFEM) and Saliency Weight Learning Module (SWLM), which aim at obtaining perception features with luminance and saliency priors at multiple scales. The supplement of luminance priors aims to perceive visually sensitive global distortion of luminance, including histogram statistical features and grayscale features with positional information. The supplement of saliency priors aims to perceive visual information that reflects local quality variation both in spatial and channel domains. Finally, to effectively model the relationship among different levels of visual information contained in the multi-scale features, the Attention Feature Fusion Module (AFFM) is proposed. Experimental results on the public UIQE and UWIQA datasets demonstrate that the proposed JLSAU outperforms existing state-of-the-art UIQA methods. | |
| 653 | |a Feature extraction | ||
| 653 | |a Visual tasks | ||
| 653 | |a Visual perception | ||
| 653 | |a Salience | ||
| 653 | |a Balances (scales) | ||
| 653 | |a Image degradation | ||
| 653 | |a Spatial discrimination | ||
| 653 | |a Modules | ||
| 653 | |a Visual stimuli | ||
| 653 | |a Attention | ||
| 653 | |a Visual effects | ||
| 653 | |a Statistical models | ||
| 653 | |a Visual perception driven algorithms | ||
| 653 | |a Quality assessment | ||
| 653 | |a Luminance | ||
| 653 | |a Image enhancement | ||
| 653 | |a Quality control | ||
| 653 | |a Distortion | ||
| 653 | |a Underwater detectors | ||
| 653 | |a Supplements | ||
| 653 | |a Algorithms | ||
| 653 | |a Image quality | ||
| 653 | |a Perception | ||
| 653 | |a Vision systems | ||
| 653 | |a Underwater | ||
| 653 | |a Temporal perception | ||
| 653 | |a Parameter estimation | ||
| 700 | 1 | |a He, Zhouyan |u College of Science and Technology, Ningbo University, Ningbo 315212, China; <email>2211170008@nbu.edu.cn</email> (Z.L.); <email>jinchongchong@nbu.edu.cn</email> (C.J.); <email>luoting@nbu.edu.cn</email> (T.L.) | |
| 700 | 1 | |a Jin, Chongchong |u College of Science and Technology, Ningbo University, Ningbo 315212, China; <email>2211170008@nbu.edu.cn</email> (Z.L.); <email>jinchongchong@nbu.edu.cn</email> (C.J.); <email>luoting@nbu.edu.cn</email> (T.L.) | |
| 700 | 1 | |a Luo, Ting |u College of Science and Technology, Ningbo University, Ningbo 315212, China; <email>2211170008@nbu.edu.cn</email> (Z.L.); <email>jinchongchong@nbu.edu.cn</email> (C.J.); <email>luoting@nbu.edu.cn</email> (T.L.) | |
| 700 | 1 | |a Chen, Yeyao |u Faculty of Information Science and Engineering, Ningbo University, Ningbo 315212, China; <email>chenyeyao@nbu.edu.cn</email> | |
| 773 | 0 | |t Remote Sensing |g vol. 16, no. 16 (2024), p. 3021 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3098193888/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3098193888/fulltextwithgraphics/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3098193888/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |