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Localized adjustments in photography have evolved significantly since the introduction of raw photo standards and AI masking tools. While AI masking promises convenience and efficiency in tasks like sky and subject selection, it can result in unnatural outcomes when compared to traditional methods. Experience shows that AI masks often require meticulous tweaking to achieve acceptable results, sometimes introducing stark edges and oversimplifications that compromise the visual integrity of landscapes. Thus, many argue that relying solely on AI may limit creativity and produce less authentic imagery compared to manual masking techniques.
AI masking revolutionizes selections but can produce unnatural images.
Photographers desire AI masking, highlighting its absence in certain software.
AI tools fail to grasp landscape complexities, often resulting in harsh edges.
AI tools in photography represent a double-edged sword; while they simplify selection processes and save time, they often overlook essential nuances that human editors traditionally incorporate. For instance, AI’s rigid boundary definitions can create unrealistic images in landscapes where soft transitions are vital for authenticity.
The current landscape of AI in photography suggests a trend towards efficiency, but practical use cases show that extensive post-processing is still necessary. Additionally, the competitive landscape among software providers indicates that traditional marketing may overlook the importance of maintaining artistic integrity in the editing process.
The discussion highlights its strengths and limitations in achieving natural-looking adjustments.
The speaker illustrates that luminance masks can create more refined and natural results than AI-based selections.
This method is preferred for creating softer transitions in landscapes compared to AI masking.
The role of Adobe's AI tools has been discussed concerning their effectiveness and the necessity of post-editing adjustments.
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The absence of AI in DXO products raises questions about its approach to image processing compared to competitors.
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