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Erschienen in:
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2018 | OriginalPaper | Buchkapitel

Patch Similarity in Transform Domain for Intensity/Range Image Denoising with Edge Preservation

verfasst von : Seema Kumari, Srimanta Mandal, Arnav Bhavsar

Erschienen in: Computer Vision, Pattern Recognition, Image Processing, and Graphics

Verlag: Springer Singapore

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Abstract

For the image denoising task, the prior information obtained from grouping similar non-local patches has been shown to serve as an effective regularizer. Nevertheless, noise may create ambiguity in grouping similar patches, hence it may degrade the results. However, most of the non-local similarity based approaches do not take care of the issue of noisy grouping. Hence, we propose to denoise an image by mitigating the issue of grouping non-local similar patches in presence of noise in transform domain using sparsity and edge preserving constraints. The effectiveness of the transform domain grouping of patches is utilized for learning dictionaries, and is further extended for achieving an initial approximation of sparse coefficient vector for the clean image patches. We have demonstrated the results of effective grouping of similar patches in denoising intensity as well as range images.

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Vorheriges Kapitel GMM Based Single Depth Image Super-Resolution
Nächstes Kapitel Multi-modal Image Analysis for Plant Stress Phenotyping
Fußnoten
1
Note that the mean intensity values of patches are used on for this particular demonstration, for simplicity of interpretation. In our actual approach we use the vectorized patch values compute patch similarity using l2 norms.
 
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Metadaten
Titel
Patch Similarity in Transform Domain for Intensity/Range Image Denoising with Edge Preservation
verfasst von
Seema Kumari
Srimanta Mandal
Arnav Bhavsar
Copyright-Jahr
2018
Verlag
Springer Singapore
Buch
Computer Vision, Pattern Recognition, Image Processing, and Graphics

Print ISBN: 978-981-13-0019-6

Electronic ISBN: 978-981-13-0020-2

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-981-13-0020-2_23