# Pixel Interaction Model for Contrast Enhancement: Bridging Social Science and Image Processing
## 5 Conclusions  
This study has presented a contrast enhancement technique of images based on opinion dynamics and social influence. An opinion dynamics-based approach is proposed here, which emulates human interaction patterns that are dictated by opinion dynamics. Agents that hold similar opinions exert a positive social influence on each other, increasing the convergence of opinions. Agents that are very different have a negative social influence on each other, such that the agents move to more extreme positions. In the following analogy, for every agent, there is a corresponding pixel, and the disagreement between two agents is represented by a difference in pixel intensity. When the pixel intensities are similar, it shows that the opinions are alike; therefore, values are altered in such a way that these similarities are reduced, and the intensities become uniform. On the opposite extreme, if the levels of intensities are very different, this may be viewed as dissimilar opinions, and changes in the intensities widen these discrepancies. These changes in the image effectively bring out distinctions between light and dark regions, sharpen the edges, and enhance contrast. Among these improvements are enhancing features and details by sharpening the picture by increasing the differences in the regions of the image where these are most pronounced.  
The results obtained by the proposed methodology were compared to results obtained by the AVHEQ [19], BBHE [18], ESIHE [20], MMBBEBHE [17], and RSESIHE [16] methods. The comparison was conducted both visually and numerically using six performance metrics: \(RMS,\ E,\ MSE,\ AME,\ SNR,\) and \(WSNR\). A representative subset of images that require a complex level of enhancement has been selected for this comparison to provide a complete and consistent evaluation.  
The results of the experiments showed that the approach with the proposed methodology produces competitive results, both visually and numerically. From these, it is evident that the proposed method operates as an accurate and robust approach to improve the contrast. Visually, it is possible to notice that the proposed approach generates high-contrast images where even the finest details, hardly visible in the original image, are now visible in the output. Numerical analysis also verifies that the proposed method, C-OPINION, outperforms the performance of other algorithms in most images, as indicated by the values provided by the indicators.  
**Author Contributions:** Conceptualization, E.C.; Methodology, E.C. and M.P.-C.; Software, J.L.; Validation, M.P.-C.; Investigation, B.A.R.-A. and A.L.-C.; Writing--original draft, B.A.R.-A.; Writing--review & editing, E.C., A.L.-C., J.L. and M.P.-C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors upon request.  
**Conflicts of Interest:** The authors declare no conflict of interest.