Image Processing - Does PSNR and SSIM metrics show smoothing (noise reduction) quality?

Question

For my Image Processing class project, I am filtering an image with various filter algorithms (bilateral filter, NL-Means etc..) and trying to compare results with changing parameters. I came across PSNR and SSIM metrics to measure filter quality but could not fully understand what the values mean. Can anybody help me about:

• Does a higher PSNR value means higher quality smoothing (getting rid of noise)?
• Should SSIM value be close to 1 in order to have high quality smoothing?
• Are there any other metrics or methods to measure smoothing quality?

I am really confused. Any help will be highly appreciated. Thank you.

Answer 1

Regarding this article:
http://icpr2010.org/pdfs/icpr2010_WeAT8.44.pdf
I found out that the PSNR can be obtained by SSIM and vice-versa. And PSNR is more sensitive to the noise than SSIM. By the other hand the other paramethers are almost equal in sensitivity by both: Gaussian Blur and discriminating Quality.

Answer 2

Expanding on @sansuiso's answer

There are a lot of others Image quality measures you can use to evaluate the de-noising capability of various filters in your case NL means , bilateral filter etc

Here is a chart that demonstrates the various parameters that could be used

Yes and more the PSNR better is the de- noising capability

Here is a paper where you can find the details regarding these parameters and the MATLAB codes could be found here

PSNR is the evaluation standard of the reconstructed image quality, and is important feature

The large the value of NAE means that image is poor quality

The large value of SC means that image is a poor quality.

Answer 3

With respect to an ideal result image, the PSNR computes the mean squared reconstruction error after denoising. Higher PSNR means more noise removed. However, as a least squares result, it is slightly biased towards over smoothed (= blurry) results, i.e. an algorithm that removes not only the noise but also a part of the textures will have a good score.

SSIm has been developed to have a quality reconstruction metric that also takes into account the similarity of the edges (high frequency content) between the denoised image and the ideal one. To have a good SSIM measure, an algorithm needs to remove the noise while also preserving the edges of the objects.

Hence, SSIM looks like a "better quality measure", but it is more complicated to compute (and the exact formula involves one number per pixel, while PSNR gives you an average value for the whole image).