Which kind of interpolation best for resizing image?

Question

I have a numpy array that I wish to resize using opencv. Its values range from 0 to 255. If I opt to use cv2.INTER_CUBIC, I may get values outside this range. This is undesirabl

Answer 1

To overcome such problem you should find out the new size of the given image where the interpolation can be made. And copy interpolated sampled image on the target image like:

# create target image and copy sample image into it
(wt, ht) = imgSize # target image size
(h, w) = img.shape # given image size
fx = w / wt
fy = h / ht
f = max(fx, fy)
newSize = (max(min(wt, int(w / f)), 1),
           max(min(ht, int(h / f)), 1))  # scale according to f (result at least 1 and at most wt or ht)
img = cv2.resize(img, newSize, interpolation=cv2.INTER_CUBIC) #INTER_CUBIC interpolation
target = np.ones([ht, wt]) * 255  # shape=(64,800)
target[0:newSize[1], 0:newSize[0]] = img

Some of the possible interpolation in openCV are:

• INTER_NEAREST – a nearest-neighbor interpolation
• INTER_LINEAR – a bilinear interpolation (used by default)
• INTER_AREA – resampling using pixel area relation. It may be a preferred method for image decimation, as it gives moire’-free results. But when the image is zoomed, it is similar to the INTER_NEAREST method.
• INTER_CUBIC – a bicubic interpolation over 4×4 pixel neighborhood
• INTER_LANCZOS4 – a Lanczos interpolation over 8×8 pixel neighborhood

See here for results in each interpolation.