Algorithms for the resizing of binary and grayscale images using a logical transform

TLDR: Algorithms for the resizing of images based on the analysis of the sum of primary implicants representation of image data, as generated by a logical transform are presented.

Abstract: The resizing of data, either upscaling or downscaling based on need for increased or decreased resolution, is an important signal processing technique due to the variety of data sources and formats used in today's world. Image interpolation, the 2D variation, is commonly achieved through one of three techniques: nearest neighbor, bilinear interpolation, or bicubic interpolation. Each method comes with advantages and disadvantages and selection of the appropriate one is dependent on output and situation specifications. Presented in this paper are algorithms for the resizing of images based on the analysis of the sum of primary implicants representation of image data, as generated by a logical transform. The most basic algorithm emulates the nearest neighbor technique, while subsequent variations build on this to provide more accuracy and output comparable to the other traditional methods. Computer simulations demonstrate the effectiveness of these algorithms on binary and grayscale images.