Abstract
The mean squared error (MSE) and its related metrics such as peak signal to noise ratio (PSNR), root mean squared error (RMSE) and signal to noise ratio (SNR) have been the basis for mathematically defined image quality measurement for a long time. These methods are all based on the MSE. Denoised image quality has also been traditionally measured in terms of the MSE or its derivatives. However, none of these metrics takes the structural fidelity of the image into account. We investigate the structural changes that occur during the denoising process and attempt to study an alternative metric for determining the quality of denoised images based on structural changes. We also show the shortcomings of the MSE-based image quality metrics.
| Original language | English |
|---|---|
| Pages (from-to) | 423-434 |
| Number of pages | 12 |
| Journal | International Journal of Circuits, Systems and Signal Processing |
| Volume | 5 |
| Issue number | 4 |
| Publication status | Published - 2011 |
| Externally published | Yes |
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Keywords
- Denoise
- Metric
- MSE
- Noise
- PSNR
- SSIM
- TV
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Signal Processing
Cite this
An investigation on the quality of denoised images. / Ndajah, Peter; Kikuchi, Hisakazu; Yukawa, Masahiro; Watanabe, Hidenori; Muramatsu, Shogo.
In: International Journal of Circuits, Systems and Signal Processing, Vol. 5, No. 4, 2011, p. 423-434.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - An investigation on the quality of denoised images
AU - Ndajah, Peter
AU - Kikuchi, Hisakazu
AU - Yukawa, Masahiro
AU - Watanabe, Hidenori
AU - Muramatsu, Shogo
PY - 2011
Y1 - 2011
N2 - The mean squared error (MSE) and its related metrics such as peak signal to noise ratio (PSNR), root mean squared error (RMSE) and signal to noise ratio (SNR) have been the basis for mathematically defined image quality measurement for a long time. These methods are all based on the MSE. Denoised image quality has also been traditionally measured in terms of the MSE or its derivatives. However, none of these metrics takes the structural fidelity of the image into account. We investigate the structural changes that occur during the denoising process and attempt to study an alternative metric for determining the quality of denoised images based on structural changes. We also show the shortcomings of the MSE-based image quality metrics.
AB - The mean squared error (MSE) and its related metrics such as peak signal to noise ratio (PSNR), root mean squared error (RMSE) and signal to noise ratio (SNR) have been the basis for mathematically defined image quality measurement for a long time. These methods are all based on the MSE. Denoised image quality has also been traditionally measured in terms of the MSE or its derivatives. However, none of these metrics takes the structural fidelity of the image into account. We investigate the structural changes that occur during the denoising process and attempt to study an alternative metric for determining the quality of denoised images based on structural changes. We also show the shortcomings of the MSE-based image quality metrics.
KW - Denoise
KW - Metric
KW - MSE
KW - Noise
KW - PSNR
KW - SSIM
KW - TV
UR - http://www.scopus.com/inward/record.url?scp=80054000831&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80054000831&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:80054000831
VL - 5
SP - 423
EP - 434
JO - International Journal of Circuits, Systems and Signal Processing
JF - International Journal of Circuits, Systems and Signal Processing
SN - 1998-4464
IS - 4
ER -