Color Image Processing | Models and Methods (CIP: MM)

ISBN 10: 3725879559 / ISBN 13: 9783725879557

Published by MDPI AG, 2026

About this Item

Color Image Processing | Models and Methods (CIP: MM) | Buch | Englisch | 2026 | MDPI AG | EAN 9783725879557 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

Seller Inventory # 136373235

Bibliographic Details

• Title: Color Image Processing | Models and Methods (CIP: MM)
• Publisher: MDPI AG
• Publication Date: 2026
• Language: English
• ISBN 10: 3725879559
• ISBN 13: 9783725879557
• Binding: Buch
• Condition: Neu
• Dimensions: 250W x 175H x 24D millimeters 250 millimeters width by 175 millimeters height by 24 millimeters depth
• Weight: 950 grams
• Seller catalogs:
  • Bücher

About this title

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.