arXiv:2607.08185v1 Announce Type: cross Abstract: Enhancing images to make them visually appealing is a persistent challenge in computer vision. Many deep-learning methods train models on paired datasets to replicate expert editing styles. However, these approaches struggle with two key issues: (1) interpretability and (2) a parametrization suitable for user adjustments. To address these challenges, we present NamedCurves+, an approach inspired by the concept of Color Naming, a universal set of familiar colors widely used in software tools for intuitive editing. Our method integrates color nam
The continuous evolution of deep learning and computer vision seeks more intuitive and interpretable methods for image manipulation, moving beyond black-box approaches.
This development offers a more user-friendly and interpretable pathway for image enhancement, potentially democratizing advanced editing capabilities for a broader audience.
Image enhancement tools could become more intuitive to use, offering interpretable adjustments based on universal color concepts rather than abstract parameters.
- · Image editing software developers
- · Content creators
- · Computer vision researchers
- · Digital artists
- · Users struggling with complex editing software
- · Traditional, less interpretable image enhancement models
Image editing workflows become more efficient due to intuitive controls.
An increase in high-quality, aesthetically pleasing digital content across various platforms.
The development of highly personalized and adaptive image enhancement agents that understand user intent through natural language based on color naming.
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Read at arXiv cs.AI