4/30/2023 0 Comments Image denoiseSometimes in a small neighbourhood around it. Chance is large that the same patch may be somewhere else in the image. Consider a small window (say 5x5 window) in the image. So idea is simple, we need a set of similar images to average out the noise. Also often there is only one noisy image available. Unfortunately this simple method is not robust to camera and scene motions. Compare the final result and first frame. Then write a piece of code to find the average of all the frames in the video (This should be too simple for you now ). This will give you plenty of frames, or a lot of images of the same scene. Hold a static camera to a certain location for a couple of seconds. You can verify it yourself by a simple setup. Ideally, you should get \(p = p_0\) since mean of noise is zero. You can take large number of same pixels (say \(N\)) from different images and computes their average. Consider a noisy pixel, \(p = p_0 n\) where \(p_0\) is the true value of pixel and \(n\) is the noise in that pixel. Noise is generally considered to be a random variable with zero mean. In short, noise removal at a pixel was local to its neighbourhood. In those techniques, we took a small neighbourhood around a pixel and did some operations like gaussian weighted average, median of the values etc to replace the central element. In earlier chapters, we have seen many image smoothing techniques like Gaussian Blurring, Median Blurring etc and they were good to some extent in removing small quantities of noise. You will see different functions like cv.fastNlMeansDenoising(), cv.fastNlMeansDenoisingColored() etc.You will learn about Non-local Means Denoising algorithm to remove noise in the image.
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