Synopsis
Color information plays a crucial role in digital image processing-it is a robust descriptor with the ability to improve data compression and support scene understanding for both humans and automatic vision systems. Moreover, Color Image Processing (CIP) has become a highly active research area, with its applications spanning biomedicine, cultural heritage, remote sensing, defense, and security, among others.
This Topic was conceived to provide an overview of the state of the art and stimulate present and future directions, explicitly bridging two aspects often treated separately: mathematical modeling and the computational design of methods. Hosted across four participating journals (Applied Sciences, Entropy, Journal of Imaging, Optics), this Topic reflects the interdisciplinary nature of CIP research. Fourteen peer-reviewed open access papers, published between 2022 and 2025 and authored by researchers worldwide, are brought together in this Topic. Overall, the contributions highlight how the advances in CIP emerge from continuous interplay between model-based formulations, optimization, and learning-based architectures, with strong ties to real-world constraints and application-driven evaluation. Taken together, the papers in this reprint confirm that CIP remains a fertile domain in which theory, computation, and data-driven methods mutually reinforce one another.
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Color Image Processing: Models and Methods (CIP: MM)
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