Originally inspired by the imaging needs of UND's Open Prototype for Educational Nanosats (OPEN) satellite program. The goal of this research is to direct the choice of an image interpolation/zoom algorithm.The image is scanned and overlapping 3x3 blocks of pixels are analyzed looking for “L” patterns. Such a pattern indicates that the value of the center pixel be changed transforming the “L” pattern into a triangle pattern. We compare this approach against different types of single-frame image interpolation algorithms, such as zero-order-hold (ZOH), bilinear, bicubic, directional cubic convolution interpolation (DCCI) approach, and a neural network approach. We use the peak signal-to-noise ratio (PSNR), mean squared error (MSE), Structural Similarity Index (SSIM), and Feature Similarity Index (FSIM) as the primary means of comparison. Tests cases include gray-scale and RGB images. In all test cases the proposed method resulted in a lower MSE, higher PSNR, and higher SSIM than the other zoom methods applied. The FSIM returned a higher value in every case for the ZOH method, but this was only when FSIM was calculated to the fourth decimal. Overall, this method results in a more accurate image after zooming than the other methods.