opencv和matlab的bicubic算法有什么区别？

问题描述

使用opencv时调整大小

  img = cv2.imread（'fname.png'，0）
 res = cv2.resize（img，None，fx = 2，fy = 2，interpolation = cv2.INTER_CUBIC）
 cv2.imwrite（'scaled_cv2.png'，res）
  

和matlab的imresize

  I = imread（' fname.png'）; 
 J = imresize（I，2，'Antialiasing'，false，'Method'，'bicubic'）; 
 imwrite（J，'scaled_matlab.png'）
  

并与imagemagick的比较与

  compare -metric PSNR fname.png scaled_cv2.png diff_cv2.png 
 compare -metric PSNR fname.png scaled_matlab.png diff_matlab.png 
  

我得到完全不同的PSNR值
他们有什么不同？

解决方案

来自和OpenCV超调，但边缘更清晰，更好与真实底层高分辨率图像进行比较。

When using opencv's resize

img = cv2.imread('fname.png', 0 )
res = cv2.resize(img,None,fx=2, fy=2, interpolation = cv2.INTER_CUBIC)
cv2.imwrite('scaled_cv2.png',res)

and matlab's imresize

I = imread('fname.png');
J = imresize(I,2, 'Antialiasing', false, 'Method', 'bicubic');
imwrite(J,'scaled_matlab.png')

and comparing with imagemagick's compare with

compare -metric PSNR fname.png scaled_cv2.png diff_cv2.png
compare -metric PSNR fname.png scaled_matlab.png diff_matlab.png

I get completely different PSNR valuesWhat are they doing different?

解决方案

From Matlab's doc:

And from OpenCV's doc:

So the only explanation for this is that they use different weighting strategy to get the average value.

From Matlab imresize.m source, you can find that the kernel constant A (see Bicubic interpolation on Wikipedia) is set to -0.5, whereas it is set to -0.75 in OpenCV (see imgproc/src/imgwarp.cpp, function interpolateCubic() on github for instance).

This gives different kernel shapes for the convolution :

Therefore, you will end with slightly different results in the final interpolated image; typically more ringing artifacts and overshoot for OpenCV, but also sharper edges and a better PSNR compared with the "true" underlying high-resolution image.

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