Measuring the Complexity of Image Enhancement and Restoration Algorithms Using a Logarithmic Model

Measuring complexity is important because it delivers insight into the practicality, usability, and efficiency of imaging algorithms.Although quality assessment metrics are perceptual indices of an algorithm's effectiveness, quantifying complexity addresses the computational cost based on selected traits.Many existing enhancement and restoration algorithms can deliver high-quality results, but at the expense of heavy computational demands.This limits the utilization of such algorithms in many scenarios, especially those with resource limitations.In this context, many have used execution time (ET) as an indicator of complexity.Still, ET is not 100% precise, and a more hands-on approach should be considered.In this study, a logarithmic model is introduced to measure complexity based on ET and memory usage, providing more meaningful complexity scores.The model's outcome is a numerical value, where higher values indicate more complexity involvement.By quantifying complexity, experts can determine a balance between efficiency and practicality.This ensures that an algorithm is not only hypothetically sound but also practical for various real-world scenarios.Likewise, this facilitates fair comparison between algorithms, guiding toward both accurate and feasible answers.