Nature–inspired optimization techniques play an essential role in the field of image processing. It reduces the noise and blurring of images and also improves the image enhancement, image restoration, image segmentation, image edge detections, image generation, image fusion, image pattern recognition, image thresholding and so on. Several optimization techniques have been proposed so far for various applications of image processing. This chapter presents the short review of nature inspired optimization algorithms such as Genetic algorithm, Genetic programming, evolutionary strategies, Grey wolf optimization, Bat optimization, Ant colony optimization, Artificial Bee Colony optimization, Particle swarm optimization, Firefly optimization, Cuckoo Search Algorithm, Elephant Herding optimization, Bumble bees mating, Lion optimization, Water wave optimization, Chemical reaction optimization, Plant optimization, The raven roosting algorithm with the insight of applying optimization algorithms in advanced image processing fields.
CITATION STYLE
Jino Ramson, S. R., Lova Raju, K., Vishnu, S., & Anagnostopoulos, T. (2019). Nature inspired optimization techniques for image processing—A short review. In Intelligent Systems Reference Library (Vol. 150, pp. 113–145). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-319-96002-9_5
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