An engineer is augmenting a dataset of 4096×4096-pixel aerial drone images before training an object-detection network. The augmentation must simultaneously (1) enlarge orientation diversity, (2) keep every original pixel value unchanged (that is, no resampling or interpolation), and (3) avoid introducing any blank or padded regions so the augmented image remains the same 4096×4096 canvas. Which rotation-based augmentation strategy best satisfies all three constraints?
Randomly rotate each image by any angle between 0° and 360°, then resize back to 4096×4096 using bilinear interpolation.
Rotate each image by 90°, 180°, or 270° using only in-place transpose and flip operations.
Rotate each image by 90° counter-clockwise and then crop the largest axis-aligned square from the center.
Rotate each image by 45°, 135°, 225°, or 315° and fill the resulting corner gaps with mirrored padding.
Rotating by exact multiples of 90° can be implemented with simple array transposes and flips, so every pixel is merely relocated-no sampling or interpolation is required. Because a 4096×4096 image is square, these rotations also leave the canvas fully occupied, so no black triangles or padding appear. Rotations at arbitrary angles (or 45° increments) require warping the image; that inserts blank corners that must be padded and uses interpolation, altering pixel values. Cropping after rotation preserves pixel integrity but reduces the canvas size, violating the 4096×4096 requirement. Therefore, using 90°, 180°, or 270° rotations performed with transpose/flip operations is the only strategy that meets all three constraints.
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Why do 90°, 180°, and 270° rotations not require interpolation?
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What happens to the image when rotated at arbitrary angles like 45°?
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How does mirrored padding differ from simply leaving blank regions after rotation?