# Pixel Interaction Model for Contrast Enhancement: Bridging Social Science and Image Processing
## 1 Introduction  
The area of computational social sciences focuses on the study of human interactions, behaviors, and social structures and employs analytical techniques and computational data to model and analyze these phenomena [1]. This field utilizes computing information and data analysis to address questions and conduct research in social sciences and offers innovative perspectives and approaches to address the complex social challenges humanity is facing today [2]. The primary objective of computational social sciences is the study of human social behavior. However, this field has diverse applications and conducts research in various domains. For instance, it can address problems in other areas, such as smart energy systems [3], where technical and social factors are considered when designing and managing them. In public health [4], the dissemination of incorrect information about critical diseases through social media platforms can have catastrophic consequences, particularly in the health field, where political-social behavior is prevalent. In the environmental sector [5], disaster-monitoring applications based on social media posts use artificial intelligence to analyze the sentiments generated by these disasters. Machine learning is a powerful tool for understanding social behavior with scientific precision, and extensive studies have demonstrated its effectiveness [6]. Computational social sciences have also been used in the domain of image processing, such as the approach presented in [7], where the phenomenon of social influence is used to segment images. Social influence and opinion dynamics are two important components of computational social sciences. Social influence corresponds to the process by which an individual adjusts several elements, such as their attitudes, beliefs, or behaviors, due to the influence of others [8]. It is a prevalent phenomenon that can happen in different social situations. It can be presented from small-group interactions to extensive social movements. Opinion dynamics, on the other hand, studies the progression of opinions and beliefs among a group of individuals through time [9]. Social influence is divided into two kinds of effects: positive and negative. Several factors--including cultural context, individual factors, and the nature of the population--have an amply documented influence on these latter effects. Positive social influence takes place when people's opinions align, reinforcing their viewpoints. Negative social influence, on the other hand, takes place when opinions are different, underlining the difference in perspective [10]. These aspects may seriously impact how people interact with society: by coming into their circle with people who keep similar viewpoints or by underlining the differences with people who have a different point of view. Contrast enhancement is one of the most extensively used methods in digital image processing, aimed at improving the quality level of poor images with respect to the human eye standard. These techniques make images more suitable for their intended use, enhancing visibility and clarity, especially when the original image quality is poor. Contrast enhancement is particularly useful in different contexts [11; 12; 13; 14; 15], such as medicine, ocular detection, satellite imaging, magnetic resonance imaging, and panoramic dental radiography. In order to achieve more accurate results in these fields, the development of new and improved contrast enhancement techniques is very important. Several state-of-the-art methods have been reported to improve image contrast, such as histogram equalization, which is one of the most commonly used techniques. The primary objective of this technique is to achieve a uniform distribution of different levels of pixel intensity in the histogram.