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Machine Learning Approaches for Image Quality Improvement

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Second International Conference on Image Processing and Capsule Networks (ICIPCN 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 300))

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Abstract

The most widely used tool for learning and interpreting medical diagnoses is magnetic resonance imaging. To analyze and learn the features, the image quality must be improved by preprocessing step. The most challenging task is to perform denoising without altering the contents of image. Performing denoising can dramatically speed up the diagnostic process by addressing the various ranges of noise in magnetic resonance images to enhance the quality of images. Many widespread studies have been carried out for noise control but lags into complication. To overcome these things, the paper provides various types of noise reduction approaches in-detail. The study also includes brief elaboration of magnetic resonance imaging. Machine learning that is foremost used field for complex problems is also discussed with pros and cons of existing techniques with performance parameters to measure the noise in MR images.

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Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Lovely Professional University, Jalandhar, India

    Sujeet More & Jimmy Singla

Authors
  1. Sujeet More
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  2. Jimmy Singla
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Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, Da-Yeh University, Changhua, Taiwan

    Joy Iong-Zong Chen

  2. Departamento de Engenharia Mecanica, Universidade do Porto, Porto, Portugal

    João Manuel R. S. Tavares

  3. College of Engineering, Prince Sattan Bin Abdulaziz University, Al-Kharj, Saudi Arabia

    Abdullah M. Iliyasu

  4. Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada

    Ke-Lin Du

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More, S., Singla, J. (2022). Machine Learning Approaches for Image Quality Improvement. In: Chen, J.IZ., Tavares, J.M.R.S., Iliyasu, A.M., Du, KL. (eds) Second International Conference on Image Processing and Capsule Networks. ICIPCN 2021. Lecture Notes in Networks and Systems, vol 300. Springer, Cham. https://doi.org/10.1007/978-3-030-84760-9_5

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