Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2019 Jul 8;2(1):7.
doi: 10.1186/s42492-019-0016-7.

Brief review of image denoising techniques

Linwei Fan  1   2   3 , Fan Zhang  2 , Hui Fan  2 , Caiming Zhang  4   5   6
Affiliations
Review

Brief review of image denoising techniques

Linwei Fan et al. Vis Comput Ind Biomed Art. .

Abstract

With the explosion in the number of digital images taken every day, the demand for more accurate and visually pleasing images is increasing. However, the images captured by modern cameras are inevitably degraded by noise, which leads to deteriorated visual image quality. Therefore, work is required to reduce noise without losing image features (edges, corners, and other sharp structures). So far, researchers have already proposed various methods for decreasing noise. Each method has its own advantages and disadvantages. In this paper, we summarize some important research in the field of image denoising. First, we give the formulation of the image denoising problem, and then we present several image denoising techniques. In addition, we discuss the characteristics of these techniques. Finally, we provide several promising directions for future research.

Keywords: Convolutional neural network; Image denoising; Low-rank; Non-local means; Sparse representation.

PubMed Disclaimer

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Twelve test images from Set12 dataset
Fig. 2
Fig. 2
Visual comparisons of denoising results on Lena image corrupted by additive white Gaussian noise with standard deviation 30: a Wiener filtering [16] (PSNR = 27.81 dB; SSIM = 0.707); b Bilateral filtering [10] (PSNR = 27.88 dB; SSIM = 0.712); c PCA method [87] (PSNR = 26.68 dB; SSIM = 0.596); d Wavelet transform domain method [89] (PSNR = 21.74 dB; SSIM = 0.316); e Collaborative filtering: BM3D [55] (PSNR = 31.26 dB; SSIM = 0.845)
Fig. 3
Fig. 3
Visual comparisons of denoising results on Boat image corrupted by additive white Gaussian noise with standard deviation 50: a TV-based regularization [28] (PSNR = 22.95 dB; SSIM = 0.456); b NLM [38] (PSNR = 24.63 dB; SSIM = 0.589); c R-NL [56] (PSNR = 25.42 dB; SSIM = 0.647); d NCSR model [66] (PSNR = 26.48 dB; SSIM = 0.689); e LRA_SVD [78] (PSNR = 26.65 dB; SSIM = 0.684); f WNNM [58] (PSNR = 26.97 dB; SSIM = 0.708)
Fig. 4
Fig. 4
Visual comparisons of denoising results on Monarch image corrupted by additive white Gaussian noise with standard deviation 75: a BM3D [55] (PSNR = 23.91 dB); b WNNM [58] (PSNR = 24.31 dB); c DnCNN [106] (PSNR = 24.71 dB); d FFDNet [107] (PSNR = 24.99 dB)
See this image and copyright information in PMC

References

    1. Motwani MC, Gadiya MC, Motwani RC, Harris FC., Jr . Abstracts of GSPX. Santa Clara: Santa Clara Convention Center; 2004. Survey of image denoising techniques; pp. 27–30.
    1. Jain P, Tyagi V. A survey of edge-preserving image denoising methods. Inf Syst Front. 2016;18(1):159–170. doi: 10.1007/s10796-014-9527-0. - DOI
    1. Diwakar M, Kumar M. A review on CT image noise and its denoising. Biomed Signal Process Control. 2018;42:73–88. doi: 10.1016/j.bspc.2018.01.010. - DOI
    1. Milanfar P. A tour of modern image filtering: new insights and methods, both practical and theoretical. IEEE Signal Process Mag. 2013;30(1):106–128. doi: 10.1109/MSP.2011.2179329. - DOI
    1. Donoho DL, Johnstone IM. Ideal spatial adaptation by wavelet shrinkage. Biometrika. 1994;81(3):425–455. doi: 10.1093/biomet/81.3.425. - DOI

LinkOut - more resources