Version 1
: Received: 22 August 2024 / Approved: 22 August 2024 / Online: 22 August 2024 (16:35:56 CEST)
How to cite:
Molina-Pérez, D.; Rojas-López, A. G. Resolving Contrast and Detail Trade-Offs in Image Processing with Multi-Objective Optimization. Preprints2024, 2024081658. https://doi.org/10.20944/preprints202408.1658.v1
Molina-Pérez, D.; Rojas-López, A. G. Resolving Contrast and Detail Trade-Offs in Image Processing with Multi-Objective Optimization. Preprints 2024, 2024081658. https://doi.org/10.20944/preprints202408.1658.v1
Molina-Pérez, D.; Rojas-López, A. G. Resolving Contrast and Detail Trade-Offs in Image Processing with Multi-Objective Optimization. Preprints2024, 2024081658. https://doi.org/10.20944/preprints202408.1658.v1
APA Style
Molina-Pérez, D., & Rojas-López, A. G. (2024). Resolving Contrast and Detail Trade-Offs in Image Processing with Multi-Objective Optimization. Preprints. https://doi.org/10.20944/preprints202408.1658.v1
Chicago/Turabian Style
Molina-Pérez, D. and Alam Gabriel Rojas-López. 2024 "Resolving Contrast and Detail Trade-Offs in Image Processing with Multi-Objective Optimization" Preprints. https://doi.org/10.20944/preprints202408.1658.v1
Abstract
This article addresses the complex challenge of simultaneously enhancing contrast and detail in an image, where improving one property often compromises the other. This trade-off is tackled using a multi-objective optimization approach. Specifically, the proposal's model integrates the sigmoid transformation function and unsharp masking highboost filtering with the NSGA-II algorithm. Additionally, a posterior preference articulation is introduced to select three key solutions from the Pareto front: the maximum contrast solution, the maximum detail solution, and the knee point solution. The proposed technique is evaluated on a range of image types, including medical and natural scenes. The final solutions demonstrated significant superiority in terms of contrast and detail compared to the original images. The three selected solutions, although all are optimal, captured distinct characteristics within the images, offering different solutions according to field preferences. This highlights the method's effectiveness across different types and enhancement requirements and emphasizes the importance of the proposed preferences in different contexts.
Keywords
Multi-objective optimization; image enhancement; contrast and detail; sigmoid transformation; NSGA-II; a posterior preference articulation
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
