To address these issues, researchers at Arizona State University introduce a new image resizing algorithm based off of the principles of optical flow as utilized for inter-frame (video) interpolation. The optical flow equation insists that for every pixel in a given video frame there exists an isointense pixel in adjacent frames. For video, this amounts to the assertion that subsequent frames are reconfigurations of the same pixels. The inventors apply the optical flow equation to the adjacent rows and columns of single images. The physical basis for optical flow in video (objects are moving) is void in the static image application. However, the use of the optical flow equation in our implementation results in a method superior to the traditional bilinear and bicubic interpolators and competitive with NEDI and iNEDI and at much faster speeds and arbitrary scaling factors.
- Medical imaging
- Image viewing and processing software
- Photographic printing
- Computer graphics
- High fidelity of images
- Faster processing with less computational overhead.
- An 8x expansion of an image took 235 times longer using a conventional method than the proposed method
- A 2x expansion of an image took 100 times longer using a conventional method than the proposed method
- Accommodates arbitrary scaling factors