Three Approaches to Improve Denoising Results that Do Not Involve Developing New Denoising Methods View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2018

AUTHORS

Gabriela Ghimpeteanu , Thomas Batard , Stacey Levine , Marcelo Bertalmío

ABSTRACT

Image denoising has been a topic extensively investigated over the last three decades and, as repeatedly shown in this book, denoising algorithms have become incredibly good, so much so that many researchers have started questioning the need to further pursue this line of research. In this chapter, we argue that there is indeed room for improvement of denoising results, and we propose three different avenues to explore, none of which requires the development of new denoising methods. First, we describe how it can be better to denoise a transform of the noisy image rather than denoise the noisy image directly. We mention several possible transforms, and an open problem is to find a transform that is optimal for denoising, according to a proper image quality metric. Next, we point out the importance of having a proper noise model for JPEG pictures, so that a variance stabilization transform can be developed that transforms noise in JPEG images into additive white Gaussian noise, enabling existing denoising methods to be properly applied to the JPEG case. Finally, we highlight the fact that while virtually all denoising methods are optimized and validated in terms of the PSNR or SSIM measures, these metrics are not well correlated with perceived image quality, and therefore, it could be best to optimize the parameter values of denoising methods according to subjective testing. A remaining challenge is to develop perceptually based image quality metrics that match observer preference. More... »

PAGES

295-329

References to SciGraph publications

  • 2012. Patch Complexity, Finite Pixel Correlations and Optimal Denoising in COMPUTER VISION – ECCV 2012
  • 2011-05. Orientation-Matching Minimization for Image Denoising and Inpainting in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2013. Generalized Gradient on Vector Bundle – Application to Image Denoising in SCALE SPACE AND VARIATIONAL METHODS IN COMPUTER VISION
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    http://scigraph.springernature.com/pub.10.1007/978-3-319-96029-6_11

    DOI

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    DIMENSIONS

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