Optimization of Post-Processing Processes in Artistic Photography Using Artificial Intelligence Algorithms

Abstract

Artificial intelligence entered photographic post-processing quietly, at first as a technical convenience, and only later as something that began to interfere with creative decision-making. What initially appeared as a set of accelerators for routine edits has gradually expanded into systems that intervene in composition, texture, and even stylistic intent. This article approaches AI-assisted post-processing as a shifting practice rather than a fixed technique. Attention is directed toward how contemporary generative models reconfigure professional workflows, sometimes supporting artistic judgment and sometimes unsettling it. The analysis moves between model architectures, real deployment conditions, and everyday editorial use, where algorithmic output rarely speaks for itself. Particular emphasis is placed on moments where optimization collides with authorship, predictability, and legal accountability. Instead of searching for a single optimal solution, the study traces how photographers adapt, combine, and occasionally resist algorithmic interventions. The argument develops around the idea that efficiency gains alone do not define successful post-processing. What ultimately matters is not the technical capability itself, but the uneven way creative control and responsibility are negotiated inside everyday practice. The article speaks to practitioners and researchers who encounter AI not as a promise fully realized, but as a system still being learned, tested, and reinterpreted inside contemporary photographic production.