noise

Im new to MatLab. Been playing around and reading through the help guide but i can't seem to solve this situation. I have removed the noise by using gaussian algorithm. That was successful but I've not managed to get the image to be clear, i've tried using Richardson-Lucy deblurring algorithm but it doesn't work. Any idea how can i solve this? Thnx in advance. Here's what i've done so far. image size = 21kb image dimension = 264 x 126 img = imread('car_plate.jpg') subplot(331); imshow(img), title('Original Image') PSF = fspecial('gaussian',15,15); blur = imfilter(img,PSF,'replicate'); subplot

This is my first question on Stackoverflow, so I apologize if I word it poorly. I am writing code to take raw acceleration data from an IMU and then integrate it to update the position of an object. Currently this code takes a new accelerometer reading every milisecond, and uses that to update the position. My system has a lot of noise, which results in crazy readings due to compounding error, even with the ZUPT scheme I implemented. I know that a Kalman filter is theoretically ideal for this scenario, and I would like to use the pykalman module instead of building one myself. My first

To give some context, I have been writing a basic Perlin noise implementation in Java, and when it came to implementing seeding, I had encountered a bug that I couldn't explain. In order to generate the same random weight vectors each time for the same seed no matter which set of coordinates' noise level is queried and in what order, I generated a new seed ( newSeed ), based on a combination of the original seed and the coordinates of the weight vector, and used this as the seed for the randomization of the weight vector by running: rnd.setSeed(newSeed); weight = new NVector(2); weight

I have 2 lists with data points in them. x = ["bunch of data points"] y = ["bunch of data points"] I've generated a graph using matplotlib in python import matplotlib.pyplot as plt plt.plot(x, y, linewidth=2, linestyle="-", c="b") plt.show() plt.close() Would I be able to reduce the noise on the data? Would a Kalman filter work here? Lyken Syu It depends how you define the "noise" and how it is caused. Since you didn't provide much information about your case, I'll take your question as "how to make the curve smooth". Kalman filter can do this, but it's too complex, I'd prefer simple IIR

In general when we add noise to a signal x=rand(1,100) , this is one way sigma_2_v = 0.5; noisy_signal = rand(1,100) + sqrt(sigma_2_v)*randn(1,100); There is another method found here: Proper way to add noise For my case, I need to have the information about the variance of the noise, sigma_2_v , and generate noisy signal by varying sigma_2_v . How can I do that? Buck Thorn There are a number of possible conventions used to define a s/n ratio, a common one being based on the notion of signal and noise power. If the total power of the spectrum is p and the noise power is np , then the signal-to

I have a dataset of accelerometric data that is affected by electical spikes. I'm looking for a good method to filter out or reduce these spikes as need to calculate on these data a rolling window of FFT and other statistical indicators such as kurtosis and skewness. I can't simply delete these outliers or replace them with NaN. Sampling 2000[hz] Until now I've tried on MATLAB 2012b: Wavelet denoising (Haar wavelet) Median Filter Despike and iterpolate approach Can you suggest a proper approach to deal with these data? Download example dataset I would suggest some local smoothing. By defining

How do I add white Gaussian noise with SNR=5dB to an image using imnoise ? I know that the syntax is: J = imnoise(I,type,parameters) and: SNR = 10log 10 [var(image)/var(error image)] How do I use this SNR value to add noise to the image? Let's start by seeing how the SNR relates to the noise. Your error image is the difference between the original image and the noisy image, meaning that the error image is the noise itself. Therefore, the SNR is actually: SNR = 10log 10 [var(image)/var(noise)] For a given image and SNR=5db, the variance of the noise would be: var(noise) = var(image)/10 SNR/10 =

Using only the modules that come with a standard python 2.6 installation, would it be possible to play a simple beeping noise? If you're on a Unix terminal, you can print "\a" to get a terminal bell: >>> def beep(): ... print "\a" >>> beep() Of course, that will print a newline too… So sys.stdout.write("\a") might be better. But you get the idea. On windows: import winsound # for sound import time # for sleep winsound.Beep(440, 250) # frequency, duration time.sleep(0.25) # in seconds (0.25 is 250ms) winsound.Beep(600, 250) time.sleep(0.25) 34.4. winsound — Sound-playing interface for Windows:

I want to estimate the noise in an image. Let's assume the model of an Image + White Noise. Now I want to estimate the Noise Variance. My method is to calculate the Local Variance (3*3 up to 21*21 Blocks) of the image and then find areas where the Local Variance is fairly constant (By calculating the Local Variance of the Local Variance Matrix). I assume those areas are "Flat" hence the Variance is almost "Pure" noise. Yet I don't get constant results. Is there a better way? Thanks. P.S. I can't assume anything about the Image but the independent noise (Which isn't true for real image yet let