Assuming random Gaussian noise on an image, how would one tell which denoise method is the best quantitatively?
A lot of papers uses MSE & PSNR. However, lower MSE coulde also mean that not enough noise has been removed, thus I think that the MSE and PSNR aren't really the best way to tell.
A table of the PSNR of the original image and the image after the various denoise algorithms have been applied should be a good method to quantitatively analyze the results of the various methods. You could also calculate a deltaPSNR between the result and the noisy image.
If you have an original image without noise, you could calculate the PSNR of this image. Then you could add noise to the image, and again calculate the PSNR. Finally, after denoising, determine the PSNR again. This final PSNR can be compared to the original image to see how much like the original each result is.
Related
I have image patches from DDSM Breast Mammography that are 150x150 in size. I would like to augment my dataset by randomly cropping these images 2x times to 120x120 size. So, If my dataset contains 6500 images, augmenting it with random crop should get me to 13000 images. Thing is, I do NOT want to lose potential information in the image and possibly change ground truth label.
What would be best way to do this? Should I crop them randomly from 150x150 to 120x120 and hope for the best or maybe pad them first and then perform the cropping? What is the standard way to approach this problem?
If your ground truth contains the exact location of what you are trying to classify, use the ground truth to crop your images in an informed way. I.e. adjust the ground truth, if you are removing what you are trying to classify.
If you don't know the location of what you are classifying, you could
attempt to train a classifier on your un-augmented dataset,
find out, what the regions of the images are that your classifier reacts to,
make note of these location
crop your images in an informed way
train a new classifier
But how do you "find out, what regions your classifier reacts to"?
Multiple ways are described in Visualizing and Understanding Convolutional Networks by Zeiler and Fergus:
Imagine your classifier classifies breast cancer or no breast cancer. Now simply take an image that contains positive information for breast cancer and occlude part of the image with some blank color (see gray square in image above, image by Zeiler et al.) and predict cancer or not. Now move the occluded square around. In the end you'll get rough predictions scores for all parts of your original image (see (d) in the image above), because when you covered up the important part that is responsible for a positive prediction, you (should) get a negative cancer prediction.
If you have someone who can actually recognize cancer in an image, this is also a good way to check for and guard against confounding factors.
BTW: You might want to crop on-the-fly and randomize how you crop even more to generate way more samples.
If the 150x150 is already the region of interest (ROI) you could try the following data augmentations:
use a larger patch, e.g. 170x170 that always contains your 150x150 patch
use a larger patch, e.g. 200x200, and scale it down to 150x150
add some gaussian noise to the image
rotate the image slightly (by random amounts)
change image contrast slightly
artificially emulate whatever other (image-)effects you see in the original dataset
I have done some pre-processing including N4 Bias correction, noise removal and scaling on medical 3D MRIs, and I was asked one question:
How to evaluate the noise influence of the effectivity and robustness of the medical image segmentation? When affecting the image structure with various noise, the extracted features will be deteriorated. Such effect should be taken advantage in the context of the method
effectivity for different noise intensity.
How to evaluate the noise affect and how to justify the noise removal method used in the scientific manuscript?
I don't know if this can be helpful but I did once in classrom with nuclear magnetic resonance.
In that case we use the Shepp Logan Phantom with FFT. then we add noise to the picture (by adding random numbers with gaussian distribution).
When you transform the image back to the phantom you can see the effects of noise and sometimes artifacts (mostly due to the FFT algorithm and the window function choosed).
What I did was check the mean value of color in the image before and after, then on edges of the pahntom (skull) you can see how much is clear the passage from white to black and vice versa.
This can be easily tested with MATLAB code and the phantom. When you have the accuracy you need you can then apply the algorithm you choose on real images.
I need to make an application in iphone which needs to calculate noise, geometric deformation other distortions in an image. How to do this? I have done some image processing stuff with opencv + iphone. But I dont know how to calculate these parameters.
1) How to calculate noise in an image?
2) What is geometric deformation and how to calculate geometric deformation of an image?
3) Is geometric deformation and distortion are same parameters in terms of image filter? or any other distortions available to calculate an image is good quality or not?
Input: My image is a face image in live video stream.
I advise you to read some literature about image processing, for example Gonzalez & Woods.
1) The simplest method of noise calculation by single image is to compute standard deviation between image and its smoothed copy. For smoothing I recommend you to use simple median filter by sample of 3x3 pixels (or more). Median is non-sensitive to outbursts of data, so noice like "salt-n-pepper" won't worsen statistics.
In cases of overexposed or underexposed images such method can give you bad results, in that case you can calculate FFT of image and use a high frequency components for noise estimation.
2), 3) Calculation of geometric deformation is possible only if you know, what should be on image. For example, if you use mire (optical etalon) with quadratic grid, you can find lines on your image (for example by Canny edge detector) and compute distortion, astigmatism and some other aberrations. This could be done also if you sure that image have some straight lines.
Defocusing can be computed from analysis of edges on image or with help of image wavelet transform.
There also much more different methods for image analysing. For example, by analysis of colour image you can estimate chromatic aberration and so on.
But I repeat: in common case this operations are impossible. They all have some particular cases of application.
Read about image quality: there are no standard for this term, in every particular case you can use one or more simple characteristics to recognize whether image good or not.
In you case I'd advice you to make a lot of photos with different kind of artefacts and quality, then make simple analysis of their statistics, wavelet compositions and R-G-B components correlation. BTW, to make analysis of colour image less sensitive to its brightness I recommend you to work in HSV colorspace (but to estimate chromatic aberration you need to work exactly with RGB components).
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.
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).
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.
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.
As we know Fourier Transform is sensitive to noises(like salt and peppers),
how can it still be used for image recognization?
Is there a FT expert here?
Update to actually answer the question you asked... :) Pre-process the image with a non-linear filter to suppress the salt & pepper noise. Median filter maybe?
Basic lesson on FFTs on matched filters follows...
The classic way of detecting a smaller image within a larger image is the matched filter. Essentially, this involves doing a cross correlation of the larger image with the smaller image (the thing you're trying to recognize).
For every position in the larger image
Overlay the smaller image on the larger image
Multiply all corresponding pixels
Sum the results
Put that sum in this position in the filtered image
The matched filter is optimal where the only noise in the larger image is white noise.
This IS computationally slow, but it can be decomposed into FFT (fast Fourier transform) operations, which are much more efficient. There are much more sophisticated approaches to image matching that tolerate other types of noise much better than the matched filter does. But few are as efficient as the matched filter implemented using FFTs.
Google "matched filter", "cross correlation" and "convolution filter" for more.
For example, here's one brief explanation that also points out the drawbacks of this very oldschool image matching approach: http://www.dspguide.com/ch24/6.htm
Not sure exactly what you're asking. If you are asking about how FFT can be used for image recognition, here are some thoughts.
FFT can be used to perform image "classification". It can't be used to recognize different faces or objects, but it can be used to classify the type of image. FFT calculates the spacial frequency content of the image. So for example, natural scene, face, city scene, etc. will have different FFTs. Therefore you can classify image or even within image (e.g. aerial photo to classify terrain).
Also, FFT is used in pre-processing for image recognition. It can be used for OCR (optical character recognition) to rotate the scanned image into correct orientation. FFT of typed text has a strong orientation. Same thing for parts inspection in industrial automation.
I don't think you'll find many methods in use that rely on Fourier Transforms for image recognition.
In the case of salt and pepper noise, it can be considered high frequency noise, and thus you could low pass filter your FFT before making a comparison with the target image.
I would imagine that it would work, but that different images that are somewhat similar (like both are photographs taken outside) would register as being the same image.