opencv和matlab的bicubic算法有什么区别？ [英] what is the difference between opencv's and matlab's bicubic algorithm?

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 PSNR 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 values What are they doing different?

解决方案

From Matlab's doc:

'bicubic'

Bicubic interpolation (the default); the output pixel value is a weighted average of pixels in the nearest 4-by-4 neighborhood

And from OpenCV's doc:

INTER_CUBIC - a bicubic interpolation over 4x4 pixel neighborhood

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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