There is a method to deblur images via Wiener filter:
https://www.mathworks.com/help/images/deblurring-images-using-a-wiener-filter.html
I would like to deblur image from the video made by smartphone.
I need to estimate parameters for Wiener filter (Len, Theta, noise level).
How to do it?
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I am new to image processing and am trying to understand gaussian blur filter. I understand the meaning/idea behind using a gaussian filter and a quick google search gives a variety of different APIs to implement a gaussian filter on an image.
But what I don't understand is -> what is the difference between applying a 1D gaussian filter vs a 2D gaussian filter?
Is a 1D filter only applicable on a 1D array of input? So we need a 2D filter (or 2 1D filters) for a 2D image/signal?
Also, I am having trouble calculating the value of a gaussian filter from a given value of sigma. Assume that I have an input signal and also a value of sigma, how to go about calculating the gaussian kernel for this signal? I did come across some solutions online but I could not understand them. I know the Gaussian function but I cannot seem to understand how we would calculate the entries of the gaussian kernel given the information of input signal and sigma. Any suggestions are highly appreciated!
Please, I want to get on code in Matlab to estimate derivatives of an Image (Ix, Ixy, Ixxx) using a recursive Gaussian filter.
I am working on an image stitching app, which takes input from camera, estimates image transformation and warped input image by the estimated transformation. As shown in the following figure, image from camera are input into 2 branch of chains. However, the processing of image warping is dependent on the result of transformation estimation. My question is how I can make branch 1 wait for the results of branch 2?
If you make your image warping filter a subclass of GPUImageTwoInputFilter, this synchronization is taken care of for you.
Target the GPUImageVideoCamera instance to your feature matching / transformation estimation filter and the image warping filter, then target your feature matching / transformation estimation filter to the image warping filter. This will cause your video input to come in via the first input image and the results of your feature matching and transformation estimation filter to be in the second image. GPUImageTwoInputFilter subclasses only process and output a frame once input frames have been provided to both their inputs.
This should give you the synchronization you want, and be pretty straightforward to set up.
I think, you can try to use, something like dispatch_semaphore_t
Look here.
If histogram equalization is done on a poorly-contrasted image then its features become more visible. However there is also a large amount of grains/speckles/noise. using blurring functions already available in OpenCV is not desirable - i'll be doing text-detection on the image later on and the letters will get unrecognizable.
So what are the preprocessing techniques that should be applied?
Standard blur techniques that convolve the image with a kernel (e.g. Gaussian blur, box filter, etc) act as a low-pass filter and distort the high-frequency text. If you have not done so already, try cv::bilateralFilter() or cv::medianBlur(). If neither of these algorithms work, you should look into other edge-preserving smoothing algorithms.
If you imagine the image as a three-dimensional space, traditional filtering replaces the value of each pixel with the weighted average of all filters in a circle centered around the pixel. Bilateral filtering does the same, but uses a three-dimensional sphere centered at the pixel. Since a well-defined edge looks like a plateau, the sphere contains only one point and the pixel value remains unchanged. You can get a more detailed explanation of the bilateral filter and some sample output here.
I am looking for inputs on an image noise filter method. A 9-pixel median filter does not work very well with dense noise. Noise is periodic (periods of 50 lines) and additive.
Thanks,
Bi
What about filtering in the Fourier domain? If the noise is periodic then with any luck your noise will appear as a pair of nice pointy features in Fourier space, where you can filter them with a couple of Gaussians then transform back to real space and your periodic noise should be gone.
I like to use selective blurs, which finds the average of only the surrounding pixels whos values are within a certain range from the value of the center pixel.
Gimp has a weighted version of this called "selective gaussian blur" you could try to see what this looks like.