Image filtering: pyramids: Laplacian pyramid

[@lbridgetiv4]

Your videos are great! Thank you for sharing!

[@richutrapbands]

Incredible video, visuals were helpful and you explained the concept clearly

[@mrtom-a-hawk6732]

Wow such a clear and concise video! And you make sure to review material that student should know! Amazing

[@MatinRafiei]

Very clear explanation, thank you

[@afrinsultana703]

can anyone give me the correct step for the laplacian pyramid??
1. Take the image fi from stage i.
2. filter the image fi with a low pass filter and thus create the image Li
3. Downsample the image li and thus create the image fi+1
4. Calculate the difference image hi=fi-li
5. cache hi
6. Consolidate all images hi
7. Repeat the above step n times.

[@hanyfarid5019]

Here is some Python code for computing a Laplacian pyramid:
# Laplacian pyramid
import matplotlib.pyplot as plt
import numpy as np
import cv2
from scipy.signal import sepfir2d
im = plt.imread( 'mandrill.png' ) # load image
h = [1/16,4/16,6/16,4/16,1/16]; # blur filter
N = 4 # number of pyramid levels
# Gaussian pyramid
G = []
G.append(im) # first pyramid level
for k in range(1,N): # pyramid levels
im2 = np.zeros( im.shape )
for z in range(3):
im2[:,:,z] = sepfir2d( im[:,:,z], h, h ) # blur each color channel
im2 = im2[0:-1:2, 0:-1:2,:] # down-sample
im = im2
G.append(im2)
# Laplacian pyramid
L = []
for k in range(0,N-1): # pyramid levels
l1 = G[k]
l2 = G[k+1]
l2 = cv2.resize(l2, (0,0), fx=2, fy=2) # up-sample
D = l1 - l2
D = D - np.min(D) # scale in [0,1]
D = D / np.max(D) # for display purposes
L.append(D)
L.append(G[N-1])
# display pyramid
fig, ax = plt.subplots(nrows=1, ncols=N, figsize=(15, 7), dpi=72,
sharex=True, sharey=True)
for k in range(N-1,-1,-1):
ax[k].imshow(L[k])

[@afrinsultana703]

@@hanyfarid5019 Thank you so much for the reply would you please give me the correct algorithm step for this one?