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Structural Similarity-Optimal Total Variation Algorithm for Image Denoising

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Foundations and Practical Applications of Cognitive Systems and Information Processing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 215))

Abstract

Image denoising is a traditional problem which has been tackled using a variety of conceptual frameworks and computational tools. Total variation-based methods have proven to be efficacious toward solving image noise removal problems. Its purpose is to remove unnecessary detail and achieve optimal performance in terms of mean squared error (MSE), a metric that has been widely criticized in the literature due to its poor performance as an image visual quality assessment. In this work, we use structural similarity (SSIM) index, a more accurate perceptual image measure, by incorporating it into the total variation framework. Specifically, the proposed optimization problem solves the problem of minimizing the total gradient norm of restored image and at the same time maximizing the SSIM index value between input and reconstructed images. Furthermore, a gradient descent algorithm is developed to solve this unconstrained minimization problem and attain SSIM-optimal reconstructed images. The image denoising experiment results clearly demonstrate that the proposed SSIM-optimal total variation algorithm achieves better SSIM performance and better perceptual quality than the corresponding MSE-optimal method.

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Acknowledgments

This study was funded by National Basic Research Program of China (973 Program) under Grant 2012CB821206.

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Authors and Affiliations

  1. State Key Laboratory of Intelligence Technology and Systems, Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, People’s Republic of China

    Yu Shao, Fuchun Sun, Hongbo Li & Ying Liu

Authors
  1. Yu Shao
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  2. Fuchun Sun
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  3. Hongbo Li
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  4. Ying Liu
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Corresponding author

Correspondence to Yu Shao .

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Editors and Affiliations

  1. Department of Computer Science and Technology, Tsinghua University, Beijing, People's Republic of China

    Fuchun Sun

  2. College of Mechatronics and Automation, National University of Defense Technolog, Changsha, People's Republic of China

    Dewen Hu

  3. Department of Computer Science and Techn, Tsinghua University, Beijing, People's Republic of China

    Huaping Liu

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Shao, Y., Sun, F., Li, H., Liu, Y. (2014). Structural Similarity-Optimal Total Variation Algorithm for Image Denoising. In: Sun, F., Hu, D., Liu, H. (eds) Foundations and Practical Applications of Cognitive Systems and Information Processing. Advances in Intelligent Systems and Computing, vol 215. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37835-5_72

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  • DOI: https://doi.org/10.1007/978-3-642-37835-5_72

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37834-8

  • Online ISBN: 978-3-642-37835-5

  • eBook Packages: EngineeringEngineering (R0)

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