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Pixel-Level Sonar Image JND Based on Inexact Supervised Learning

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Pattern Recognition and Computer Vision (PRCV 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14435))

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Abstract

The Just Noticeable Difference (JND) model aims to identify perceptual redundancies in images by simulating the perception of the Human Visual System (HVS). Exploring the JND of sonar images is important for the study of their visual properties and related applications. However, there is still room for improvement in performance of existing JND models designed for Natural Scene Images (NSIs), and the characteristics of sonar images are not sufficiently considered by them. On the other hand, there are significant challenges in constructing a densely labeled pixel-level JND dataset. To tackle these issues, we proposed a pixel-level JND model based on inexact supervised learning. A perceptually lossy/lossless predictor was first pre-trained on a coarse-grained picture-level JND dataset. This predictor can guide the unsupervised generator to produce an image that is perceptually lossless compared to the original image. Then we designed a loss function to ensure that the generated image is perceptually lossless and maximally different from the original image. Experimental results show that our model outperforms current models.

This work was supported in part by the National Natural Science Foundation of China under Grant 62171134 and in part by Natural Science Foundation of Fujian Province, China under Grant 2022J05117 and 2022J02015.

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Author information

Authors and Affiliations

  1. Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information, Fuzhou University, Fuzhou, 350108, China

    Qianxue Feng, Mingjie Wang, Weiling Chen & Tiesong Zhao

  2. Key Laboratory of Underwater Acoustic Communication and Marine Information Technology (Xiamen University), Ministry of Education, Xiamen University, Xiamen, 361005, China

    Yi Zhu

Authors
  1. Qianxue Feng
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  2. Mingjie Wang
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  3. Weiling Chen
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  4. Tiesong Zhao
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  5. Yi Zhu
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Corresponding author

Correspondence to Weiling Chen .

Editor information

Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Qingshan Liu

  2. Xiamen University, Xiamen, China

    Hanzi Wang

  3. Beijing University of Posts and Telecommunications, Beijing, China

    Zhanyu Ma

  4. Sun Yat-sen University, Guangzhou, China

    Weishi Zheng

  5. Peking University, Beijing, China

    Hongbin Zha

  6. Chinese Academy of Sciences, Beijing, China

    Xilin Chen

  7. Chinese Academy of Sciences, Beijing, China

    Liang Wang

  8. Xiamen University, Xiamen, China

    Rongrong Ji

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Feng, Q., Wang, M., Chen, W., Zhao, T., Zhu, Y. (2024). Pixel-Level Sonar Image JND Based on Inexact Supervised Learning. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14435. Springer, Singapore. https://doi.org/10.1007/978-981-99-8552-4_37

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  • DOI: https://doi.org/10.1007/978-981-99-8552-4_37

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-8551-7

  • Online ISBN: 978-981-99-8552-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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