Research on Image Processing and Generative Teaching in the Context of AIGC

Abstract

AIGC, meaning AI-generated content, is discussed in this paper, mainly in the form of painting, without discussing the automation of music composition and text-writing poetry, etc. While generative art has aesthetic characteristics, more content needs to be studied in the context of computer information science, with a particular emphasis on computer vision and computer graphics. The article focuses on a comparative analysis of two models, diffusion algorithms and generative adversarial networks, and their application to tools. The practical part of image processing uses a combination of case studies and questionnaires to demonstrate the lack of methodology, teaching experience, and introductory learning materials for non-computer professionals in the emerging field, and to explain “filter mapping” through Processing, a visual programming software. The author’s reflections on the generated content combine the self-similarity of the “Uncanny Valley effect” and the “Dunning Kruger effect” lineage, comparing the “self-organizing” (machine) simulation personification and the “life form” (human) simulation personification. The process of cognitive assimilation of a “living organism” (human) is used to understand the new human-machine associative relationship.