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Local Image Denoising Using RAISR
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DOI：10.1109/ACCESS.2022.3152219.png)
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摘要
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Digital images are frequently degraded by Gaussian noise while capturing photos. This paper proposes a rapid and high accurate Gaussian noise removal method by applying the learned linear filter used in RAISR for super-resolution. The denoising methods are classified into local, nonlocal methods and deep-learning-based methods. The conventional local processing has a problem that high-frequency components of the original image are lost while reducing the noise. The nonlocal and deep-learning-based methods achieve higher denoising performance but take a long time for training and implementation. To solve these problems, we apply a super-resolution method to the local denoising method as post-processing because it can efficiently recover the high-frequency components. The super-resolution method uses a learned linear filter according to the feature of patches. The novelty of this paper is that the same processing as super-resolution is incorporated into denoising. The proposed algorithm is a rapid local denoising method and can achieve comparable performance to the high-accurate nonlocal denoising methods. Experimental results show that our proposed method provides accurate denoising performance with a low computational cost compared to nonlocal processing like BM3D.
Keyword:
Noise reduction
Gaussian noise
Noise measurement
Image reconstruction
Superresolution
Training
Licenses
Denoising
Gaussian noise
joint bilateral filter
RAISR
super-resolution
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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