The thesis presented the field of generative artificial intelligence in the process of photographic
post-production. Based on various photographic motifs, the entire post-production process was
outlined and a comparison between traditional manual editing and contemporary approaches
based on artificial intelligence was conducted.
In the theoretical part, the development of photography and post-production was presented,
together with an overview of existing technologies, tools, and software. Manual editing tools
such as Adobe Photoshop and Adobe Lightroom were described, as well as modern tools
incorporating generative artificial intelligence, including Adobe Firefly, ChatGPT, and Gemini.
In the experimental part, a comparison of different methods of photographic post-processing
was carried out with regard to time efficiency, the number of processing steps, and the final
visual result. The processed photographs were further analysed through a survey in which
participants evaluated their visual appeal. Based on the analysis of the results, it was
determined which approaches enable faster processing and which achieve better results in
terms of user perception.
The results showed that approaches based on generative artificial intelligence enable faster
and more efficient photo editing and simplify the workflow. However, it was found that manual
editing in certain cases achieves a higher level of quality, particularly in terms of precision and
control over the final image. Based on the findings, it was concluded that generative artificial
intelligence represents an important supportive tool in photographic post-production, but
cannot fully replace the role of the photographer.
|
