Resolving Contrast and Detail Trade-Offs in Image Processing with Multi-Objective Optimization
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 trans...
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MDPI AG
2024-11-01
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| Series: | Mathematical and Computational Applications |
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| Online Access: | https://www.mdpi.com/2297-8747/29/6/104 |
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| author | Daniel Molina-Pérez Alam Gabriel Rojas-López |
| author_facet | Daniel Molina-Pérez Alam Gabriel Rojas-López |
| author_sort | Daniel Molina-Pérez |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-62bd0226c1ce46edadca69e395f42bb2 |
| institution | OA Journals |
| issn | 1300-686X 2297-8747 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematical and Computational Applications |
| spelling | doaj-art-62bd0226c1ce46edadca69e395f42bb22025-08-20T02:00:34ZengMDPI AGMathematical and Computational Applications1300-686X2297-87472024-11-0129610410.3390/mca29060104Resolving Contrast and Detail Trade-Offs in Image Processing with Multi-Objective OptimizationDaniel Molina-Pérez0Alam Gabriel Rojas-López1Escuela Superior de Cómputo, Instituto Politécnico Nacional, Ciudad de México 07700, MexicoCentro de Innovación y Desarrollo Tecnológico en Cómputo, Instituto Politécnico Nacional, Ciudad de México 07700, MexicoThis 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.https://www.mdpi.com/2297-8747/29/6/104multi-objective optimizationimage enhancementcontrast and detailsigmoid transformationNSGA-IIa posterior preference articulation |
| spellingShingle | Daniel Molina-Pérez Alam Gabriel Rojas-López Resolving Contrast and Detail Trade-Offs in Image Processing with Multi-Objective Optimization Mathematical and Computational Applications multi-objective optimization image enhancement contrast and detail sigmoid transformation NSGA-II a posterior preference articulation |
| title | Resolving Contrast and Detail Trade-Offs in Image Processing with Multi-Objective Optimization |
| title_full | Resolving Contrast and Detail Trade-Offs in Image Processing with Multi-Objective Optimization |
| title_fullStr | Resolving Contrast and Detail Trade-Offs in Image Processing with Multi-Objective Optimization |
| title_full_unstemmed | Resolving Contrast and Detail Trade-Offs in Image Processing with Multi-Objective Optimization |
| title_short | Resolving Contrast and Detail Trade-Offs in Image Processing with Multi-Objective Optimization |
| title_sort | resolving contrast and detail trade offs in image processing with multi objective optimization |
| topic | multi-objective optimization image enhancement contrast and detail sigmoid transformation NSGA-II a posterior preference articulation |
| url | https://www.mdpi.com/2297-8747/29/6/104 |
| work_keys_str_mv | AT danielmolinaperez resolvingcontrastanddetailtradeoffsinimageprocessingwithmultiobjectiveoptimization AT alamgabrielrojaslopez resolvingcontrastanddetailtradeoffsinimageprocessingwithmultiobjectiveoptimization |