This paper presents a Regeneration filter for reducing near Salt-and-Pepper (nS&P) noise in images, designed for selective noise removal while simultaneously preserving structural details. Unlike conventional methods, the proposed filter eliminates the need for median or other filters, focusing exclusively on restoring noise-affected pixels through localized contextual analysis in the immediate surroundings. Our approach employs an iterative processing method, where additional iterations do not degrade the image quality achieved after the first filtration, even with high noise densities up to 97% spatial distribution. To ensure the results are measurable and comparable with other methods, the filter’s performance was evaluated using standard image quality assessment metrics. Experimental evaluations across various image databases confirm that our filter consistently provides high-quality results. The code is implemented in the R programming language, and both data and code used for the experiments are available in a public repository, allowing for replication and verification of the findings.

The EU AI Act of 2024 represents the first comprehensive legal framework for regulating artificial intelligence, introducing a classification of AI systems based on risk levels and specific requirements for high-risk applications. This paper analyzes the transformative impact of this regulation on software engineering, focusing on economic aspects such as compliance costs, new opportunities for innovation, and changes in labor market dynamics. Special attention is given to the potential harmonization with the legislation of the Republic of Serbia, identifying key points of alignment and possible legal conflicts. The significance of this paper lies in its contribution to understanding how the EU AI Act shapes global AI regulation and provides a framework for adapting local legislation in Serbia, thereby fostering technological and legal advancement.

The European Union’s Artificial Intelligence Act (EU AI Act), adopted in 2024, and introduced the first comprehensive legal framework for regulating artificial intelligence across sectors, including education. This paper explores the dual implications of the Act—contractual and pedagogical—within the context of digital education, with a particular emphasis on IT curricula. It examines the risks of diminishing foundational knowledge in algorithmic thinking, programming, and system architecture as AI tools become more prevalent in academic settings. The paper analyzes legal obligations arising from the use of AI in education, such as transparency, informed consent, and liability, situating them within the law of obligations and contract law. It argues that AI integration in education, particularly in high-risk domains, requires more than technical compliance: it demands sustainable, ethical, and pedagogically informed implementation. Drawing on EU policies, UNESCO frameworks, and recent academic literature, the paper proposes strategic recommendations to ensure that digital education not only complies with legal standards but also upholds the core mission of higher education—critical thinking, ethical responsibility, and the cultivation of foundational knowledge.

This research paper gives a chronological overview of the median filter development, as one of the filters recommended for digital image processing

Segmentation is one of the latest directions of digital imaging development, presented by partial segments which are parts of the same image. The currently used algorithms are rare and far from ideal. Depending on the situation,...

Segmentation Techniques based on Image Quality and Edge Detection Algorithms