Nearest Neighbor Classification for Classical Image Upsampling
This addresses the need for efficient and high-quality image upscaling, but appears incremental as it applies nearest neighbor classification, a known method, to this task.
The paper tackles the problem of image upsampling by aiming to improve resolution with realistic detail while maintaining computational efficiency comparable to lossy methods. The result is evaluated based on passing a human test for believability and realism.
Given a set of ordered pixel data in the form of an image, our goal is to perform upsampling on the data such that: the resulting resolution is improved by some factor, the final result passes the human test, having added new, believable, and realistic information and detail to the image, the time complexity for upscaling is relatively close to that of lossy upscaling implementations.