## High Pass Filter for image processing in python by using scipy/numpy

gaussian filter python

image filtering python

opencv high-pass filter

high pass filter image processing

laplacian filter python

low pass filter python

remove noise from image python

I am currently studying image processing. In Scipy, I know there is one median filter in Scipy.signal. Can anyone tell me if there is one filter similar to high pass filter?

Thank you

"High pass filter" is a very generic term. There are an infinite number of different "highpass filters" that do very different things (e.g. an edge dectection filter, as mentioned earlier, is technically a highpass (most are actually a bandpass) filter, but has a very different effect from what you probably had in mind.)

At any rate, based on most of the questions you've been asking, you should probably look into `scipy.ndimage`

instead of `scipy.filter`

, especially if you're going to be working with large images (ndimage can preform operations in-place, conserving memory).

As a basic example, showing a few different ways of doing things:

import matplotlib.pyplot as plt import numpy as np from scipy import ndimage import Image def plot(data, title): plot.i += 1 plt.subplot(2,2,plot.i) plt.imshow(data) plt.gray() plt.title(title) plot.i = 0 # Load the data... im = Image.open('lena.png') data = np.array(im, dtype=float) plot(data, 'Original') # A very simple and very narrow highpass filter kernel = np.array([[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]]) highpass_3x3 = ndimage.convolve(data, kernel) plot(highpass_3x3, 'Simple 3x3 Highpass') # A slightly "wider", but sill very simple highpass filter kernel = np.array([[-1, -1, -1, -1, -1], [-1, 1, 2, 1, -1], [-1, 2, 4, 2, -1], [-1, 1, 2, 1, -1], [-1, -1, -1, -1, -1]]) highpass_5x5 = ndimage.convolve(data, kernel) plot(highpass_5x5, 'Simple 5x5 Highpass') # Another way of making a highpass filter is to simply subtract a lowpass # filtered image from the original. Here, we'll use a simple gaussian filter # to "blur" (i.e. a lowpass filter) the original. lowpass = ndimage.gaussian_filter(data, 3) gauss_highpass = data - lowpass plot(gauss_highpass, r'Gaussian Highpass, $\sigma = 3 pixels$') plt.show()

**Scripts/highpassFilter.py at master · miura/Scripts · GitHub,** Image pre-processing involves applying image filters to an image. import numpy as npimport cv2from matplotlib import pyplot as pltfrom PIL import Image, Low pass filters and high pass filters are both frequency filters. High Pass Filter for image processing in python by using scipy/numpy 46 "High pass filter" is a very generic term.

One simple high-pass filter is:

-1 -1 -1 -1 8 -1 -1 -1 -1

The Sobel operator is another simple example.

In image processing these sorts of filters are often called "edge-detectors" - the Wikipedia page was OK on this last time I checked.

**Image Filters in Python,** The next few sections demonstrate how to use different functions from the numpy , scipy , and scikit-image libraries to implement an HPF and the impact of an HPF One simple high-pass filter is: The Sobel operator is another simple example. In image processing these sorts of filters are often called "edge-detectors" - the Wikipedia page was OK on this last time I checked. True that the Laplace operator is often called an edge detector.

Here is how we can design a HPF with `scipy fftpack`

from skimage.io import imread import matplotlib.pyplot as plt import scipy.fftpack as fp im = np.mean(imread('../images/lena.jpg'), axis=2) # assuming an RGB image plt.figure(figsize=(10,10)) plt.imshow(im, cmap=plt.cm.gray) plt.axis('off') plt.show()

**Original Image**

F1 = fftpack.fft2((im).astype(float)) F2 = fftpack.fftshift(F1) plt.figure(figsize=(10,10)) plt.imshow( (20*np.log10( 0.1 + F2)).astype(int), cmap=plt.cm.gray) plt.show()

**Frequency Spectrum with FFT**

(w, h) = im.shape half_w, half_h = int(w/2), int(h/2) # high pass filter n = 25 F2[half_w-n:half_w+n+1,half_h-n:half_h+n+1] = 0 # select all but the first 50x50 (low) frequencies plt.figure(figsize=(10,10)) plt.imshow( (20*np.log10( 0.1 + F2)).astype(int)) plt.show()

**Block low Frequencies in the Spectrum**

im1 = fp.ifft2(fftpack.ifftshift(F2)).real plt.figure(figsize=(10,10)) plt.imshow(im1, cmap='gray') plt.axis('off') plt.show()

**Output Image after applying the HPF**

**High-Pass Filter (HPF),** OpenCV-Python is a library of Python bindings designed to solve This also makes it easier to integrate with other libraries that use Numpy such as SciPy High pass filtering in image processing has a plain objective that is A band-pass filter can be formed by cascading a high-pass filter and a low-pass filter. A band-reject filter is a parallel combination of low-pass and high-pass filters. Now lets see a sample data

scipy.filter contains a large number of generic filters. Something like the iirfilter class can be configured to yield the typical Chebyshev or Buttworth digital or analog high pass filters.

**Digital Image Processing: Implementing High Pass Filter using ,** Be able to apply spatial frequency filters to produce filtered images. import numpy as np import scipy.misc import psychopy.visual import psychopy.event win Compute an initial state zi for the sosfilt function that corresponds to the steady state of the step response. sosfiltfilt(sos, x[, axis, padtype, padlen]) A forward-backward filter using cascaded second-order sections. hilbert(x[, N, axis]) Compute the analytic signal, using the Hilbert transform.

You can use a Gaussian filter as it gives much sharpness than a pure HPF, for using a simple HPF you can use the following code

import numpy as np import cv2 from scipy import ndimage class HPF(object): def __init__(self, kernel, image): self.kernel = np.array(kernel) self.image = image def process(self): return ndimage.convolve(self.image, self.kernel) if __name__ == "__main__": #enter ur image location image = cv2.imread("images/test2.jpg", 0) kernel3x3 = [[-1,-1,-1],[-1,8,-1],[-1,-1,-1]] kernel5x5 = [[-1, -1, -1, -1, -1], [-1, 1, 2, 1, -1], [-1, 2, 4, 2, -1], [-1, 1, 2, 1, -1], [-1, -1, -1, -1, -1]] hpf1 = HPF(kernel3x3, image) hpfimage1 = hpf1.process() hpf2 = HPF(kernel5x5, image) hpfimage2 = hpf2.process() cv2.imshow("3x3",hpfimage1) cv2.imshow("5x5",hpfimage2) cv2.waitKey() cv2.destroyAllWindows()

To use the Gaussian filter just add the Gaussian blur to your image

blurred = cv2.GaussianBlur(image, (11, 11), 0)

Then minus it from the original image

g_hpf = image - blurred

Original code taken from : Image Sharpening by High Pass Filter using Python and OpenCV

**Spatial frequency filtering,** I am currently studying image processing. In Scipy, I know there is one median filter in Scipy.signal. Can anyone tell me if there is one filter similar to high pass Perform a Wiener filter on an N-dimensional array. symiirorder1 Implement a smoothing IIR filter with mirror-symmetric boundary conditions using a cascade of first-order sections. symiirorder2 Implement a smoothing IIR filter with mirror-symmetric boundary conditions using a cascade of second-order sections. lfilter (b, a, x[, axis, zi])

**Multidimensional image processing (scipy ,** Image processing and analysis are generally seen as operations on 2-D Take, for example, the correlation of a 1-D array with a filter of length 3 consisting of ones: be used to pass a tuple of extra arguments and a dictionary of named where the actual filtering operation must be supplied as a python The difference-equation filter is called using the command lfilter in SciPy. This command takes as inputs the vector b, the vector, a, a signal x and returns the vector y (the same length as x ) computed using the equation given above. If x is N -dimensional, then the filter is computed along the axis provided.

**Smoothing Images,** Docs »; OpenCV-Python Tutorials »; Image Processing in OpenCV Blur imagess with various low pass filters; Apply custom-made filters to As for one-dimensional signals, images also can be filtered with various low-pass filters (LPF), high-pass import cv2 import numpy as np from matplotlib import pyplot as plt img scipy.signal.order_filter¶ scipy.signal.order_filter (a, domain, rank) [source] ¶ Perform an order filter on an N-dimensional array. Perform an order filter on the array in. The domain argument acts as a mask centered over each pixel. The non-zero elements of domain are used to select elements surrounding each input pixel which are placed in

**Solving Some Image Processing Problems with Python libraries ,** Python image processing libraries are going to be used to solve these problems. At each iteration create an image by using a linear combination of the two image numpy ndarrays given by. 3. Since the Gaussian blur is a low-pass filter, it removes the high from scipy.ndimage import gaussian_filter. Interpolation (scipy.interpolate) Input and output (scipy.io) Linear algebra (scipy.linalg) Miscellaneous routines (scipy.misc) Multi-dimensional image processing (scipy.ndimage) Orthogonal distance regression (scipy.odr) Optimization and root finding (scipy.optimize) Signal processing (scipy.signal) Sparse matrices (scipy.sparse)

##### Comments

- Thanks for a great script! I learned a lot about convolve() and matplotlib
*and*even Python. (I had no idea something like "plot.i" could work.) - isn't a Gaussian filter a low pass filter?
- @A.H. - Yes, but if you subtract the gaussian lowpass from the original image, you get an equivalent highpass filter. That's what's referred to as a "gaussian high pass". (Have a look at the comments above the code for that portion.)
- Given the return of convolve, it's possible to get the dx and dy?
- True that the Laplace operator is often called an edge detector. Sad, really, because it isn’t. It is really good at detecting lines and dots, not so much edges (as in: it doesn’t give a high response at edges, it tends to be zero there).