cse cse - 1 year ago 117
Python Question

Reconstructed Image after Laplacian Pyramid Not the same as original image

I am converting an RGB image into YCbCr and then want to compute the laplacian pyramid for the same. After color conversion, I am experimenting with the code give on the Image Pyramid tutorial of OpenCV to find the Laplacian pyramid of an image and then reconstruct the original image. However, if I increase the number of levels in my code to a higher number, say 10, then the reconstructed image(after conversion back to RGB) does not look the same as the original image(image looks blurred - please see below link for the exact image). I am not sure why this is happening. Is it suppose to happen when the levels increase or is there anything wrong in the code?

frame = cv2.cvtColor(frame_RGB, cv2.COLOR_BGR2YCR_CB)
height = 10
Gauss = frame.copy()
gpA = [Gauss]
for i in xrange(height):
Gauss = cv2.pyrDown(Gauss)

lbImage = [gpA[height-1]]

for j in xrange(height-1,0,-1):
GE = cv2.pyrUp(gpA[j])
L = cv2.subtract(gpA[j-1],GE)

ls_ = lbImage[0]
for j in range(1,height,1):
ls_ = cv2.pyrUp(ls_)
ls_ = cv2.add(ls_,lbImage[j])

ls_ = cv2.cvtColor(ls_, cv2.COLOR_YCR_CB2BGR)
cv2.imshow("Pyramid reconstructed Image",ls_)

For reference, please see the reconstructed image and the original image.

Reconstructed Image

Original Image

Answer Source

pyrDown blurs an image and downsamples it, loosing some information. Saved pyramid levels (gpA[] here) contain smaller and smaller image matrices, but don't keep rejected information details (high-frequency ones).

So reconstructed image cannot show all original details

From tutorial: Note: When we reduce the size of an image, we are actually losing information of the image.

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