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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 undesirable, since the resized array is supposed to still represent an image. One solution is to clip the results to [0, 255]. Another is to use a different interpolation method. It is my understanding that using INTER_AREA is valid for down-sampling an image, but works similar to nearest neighbor for upsampling it, rendering it less than optimal for my purpose.
Should I use INTER_CUBIC (and clip), INTER_AREA, or INTER_LINEAR?
an example for values outside of range using INTER_CUBIC:
a = np.array( [ 0, 10, 20, 0, 5, 2, 255, 0, 255 ] ).reshape( ( 3, 3 ) ) [[ 0 10 20] [ 0 5 2] [255 0 255]] b = cv2.resize( a.astype('float'), ( 4, 4 ), interpolation = cv2.INTER_CUBIC ) [[ 0. 5.42489886 15.43670964 21.29199219] [ -28.01513672 -2.46422291 1.62949324 -19.30908203] [ 91.88964844 25.07939219 24.75106835 91.19140625] [ 273.30322266 68.20603609 68.13853455 273.15966797]] Edit: As berak pointed out, converting the type to float (from int64) allows for values outside the original range. the cv2.resize() function does not work with the default 'int64' type. However, converting to 'uint8' will automatically saturate the values to [0..255].
Also, as pointed out by SaulloCastro, another related answer demonstrated scipy's interpolation, and that there the defualt method is the cubic interpolation (with saturation).
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Image interpolation occurs when you resize or distort your image from one pixel grid to another. Image resizing is necessary when you need to increase or decrease the total number of pixels, whereas remapping can occur when you are correcting for lens distortion or rotating an image.
Image interpolation is generally achieved through one of three methods: nearest neighbor, bilinear interpolation, or bicubic interpolation.
Lanczos-3 interpolation clearly provides the best result. It is the default algorithm used in all our standard tools for image upsampling tasks. Bicubic spline interpolation is acceptable, but less accurate than Lanczos and leads to significant dispersion of small-scale bright structures.
Image SizeTurn on the "Constrain Proportions" check box to resize your image by the same percentage both horizontally and vertically. Turn on the "Resample Image" check box to tell Photoshop you want to enlarge and interpolate your file, not just reinterpret its dimensions by changing its resolution.
If you are enlarging the image, you should prefer to use INTER_LINEAR or INTER_CUBIC interpolation. If you are shrinking the image, you should prefer to use INTER_AREA interpolation.
Cubic interpolation is computationally more complex, and hence slower than linear interpolation. However, the quality of the resulting image will be higher.
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