# Pixel Interaction Model for Contrast Enhancement: Bridging Social Science and Image Processing
## 1 Introduction
Among the most utilized equalizers are the recursive sub-image histogram equalization applied to grayscale images (RSIHE) [16], the minimum mean brightness error bi-histogram equalization in contrast enhancement (MMBEBHE) [17], the brightness preserving bi-histogram equalization (BBHE) [18], the averaging histogram equalization (AVHEQ) [19], and the exposure-based sub-image histogram equalization (ESIHE) [20]. However, histogram equalization does not always work well because equalization has several disadvantages. Enhancement of images can result in many cases in the introduction of unwanted details, such as existing noise amplification, resulting in an image of poorer visual quality. At other times, the method may produce images that appear unnatural at first glance, and in some cases, it can alter the appearance of essential features, such as textures or edges, among others. Other widely used methods for image contrast enhancement use metaheuristic algorithms, which perform the search through all the feasible transformations to find the best option regarding the original image. Normally, these methods involve the design of a mathematical function of transformation and trying to find the best parameters that fulfill the best performance function, which generally calculates quality metrics regarding the image [21; 22; 23; 24; 25]. The result of this process will be the selection of the best transformation, the one that yields the optimal global performance and results in an improved image compared to the original. Although promising results have been obtained with metaheuristic algorithms, the design and realization of such algorithms is a difficult task because one needs to model not only the mathematical function of transformation but also the function of performance evaluating the quality of every resultant transformed image. Besides, the most suitable metaheuristic algorithm will need to be further selected and adapted to process both functions. This paper presents a novel approach to enhance image contrast inspired by opinion dynamics and social influence. By integrating insights from social sciences, image processing, and computer vision, this method offers new perspectives on complex social phenomena. The proposed technique mimics human interaction patterns influenced by opinion dynamics. In a positive social influence situation, if people have similar opinions, they reinforce each other, creating further alignment of their views. In situations where the differences in opinions are huge, there is negative social influence whereby people take more polarized positions. In this analogy, each pixel represents one's opinion, and the disagreement between two persons is represented as a difference in pixel intensities. If these pixel intensities are similar, they reflect similar opinions; hence, the values are fitted such that this difference is reduced to obtain equal intensities. However, when intensity levels are significantly different, indicating opposing opinions, the intensities are modified to emphasize their differences. These modifications in the image can successfully differentiate between light and dark regions, accentuate edges, and increase the overall contrast. These changes enhance features and details by increasing the dissimilarity in those areas, hence creating a much sharper and more pleasing image. Its potential to enhance visual quality was established in various experiments using diverse image datasets and quality metrics. A comprehensive examination of the outcomes discloses that this method surpasses other techniques in improving contrast. Although social influence models have previously been applied in the field of image processing [7], their use has been primarily focused on segmentation. In our approach, we extend the application of these models to address the problem of contrast enhancement in images.