The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.
Is Denoising Dead?
Abstract
Image denoising has been a well studied problem in the field of image processing. Yet researchers continue to focus attention on it to better the current state-of-the-art. Recently proposed methods take different approaches to the problem and yet their denoising performances are comparable. A pertinent question then to ask is whether there is a theoretical limit to denoising performance and, more importantly, are we there yet? As camera manufacturers continue to pack increasing numbers of pixels per unit area, an increase in noise sensitivity manifests itself in the form of a noisier image. We study the performance bounds for the image denoising problem. Our work in this paper estimates a lower bound on the mean squared error of the denoised result and compares the performance of current state-of-the-art denoising methods with this bound. We show that despite the phenomenal recent progress in the quality of denoising algorithms, some room for improvement still remains for a wide class of general images, and at certain signal-to-noise levels. Therefore, image denoising is not dead--yet.
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Cites
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Cited by
FFDNet: Toward a Fast and Flexible Solution for CNN-Based Image Denoising
Zhang, K., Zuo, W., & Zhang, L. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2839891 · Cited by 2,678
Toward Convolutional Blind Denoising of Real Photographs
Guo, S., Yan, Z., Zhang, K., Zuo, W., & Zhang, L. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00181 · Cited by 1,221
The Little Engine That Could: Regularization by Denoising (RED)
Romano, Y., Elad, M., & Milanfar, P. (2017) · SIAM Journal on Imaging Sciences · doi:10.1137/16m1102884 · Cited by 934
Self-citationReal Image Denoising With Feature Attention
Anwar, S., & Barnes, N. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00325 · Cited by 784
Graph Laplacian Regularization for Image Denoising: Analysis in the Continuous Domain
Pang, J., & Cheung, G. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2651400 · Cited by 257
Metadata
- Authors
- Chatterjee, P., & Milanfar, P.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2010-04
- Volume
- 19
- Issue
- 4
- Pages
- 895-911
- ISSN
- 1057-7149 (ISSN-L)
- 1941-0042
- OpenAlex
- W1997147589
- Cited by
- 422
- References
- 45
- FWCI
- 20.52
- Type
- journal article
- Language
- en
- Open access
- closed
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