Support vector regression based image denoising
Image and Vision Computing
Image Enhancement Using Multi-objective Genetic Algorithms
PReMI '09 Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence
A novel evolutionary approach to image enhancement filter design: method and applications
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Boolean derivatives with application to edge detection for imaging systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Morphological image enhancement procedure design by using genetic programming
Proceedings of the 13th annual conference on Genetic and evolutionary computation
A novel genetic programming algorithm for designing morphological image analysis method
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part I
Digital images enhancement with use of evolving neural networks
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Automatic image enhancement driven by evolution based on ridgelet frame in the presence of noise
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
Non-linear grayscale image enhancement based on firefly algorithm
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part II
Comparative analysis of evolutionary algorithms for image enhancement
International Journal of Metaheuristics
An efficient algorithm for gray level image enhancement using cuckoo search
SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
Contrast enhancement of fog and haze stereo images based on mobile computing
International Journal of Wireless and Mobile Computing
Variance as a Stopping Criterion for Genetic Algorithms with Elitist Model
Fundamenta Informaticae
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Image enhancement is the task of applying certain transformations to an input image such as to obtain a visually more pleasant, more detailed, or less noisy output image. The transformation usually requires interpretation and feedback from a human evaluator of the output result image. Therefore, image enhancement is considered a difficult task when attempting to automate the analysis process and eliminate the human intervention. This paper introduces a new automatic image enhancement technique driven by an evolutionary optimization process. We propose a novel objective criterion for enhancement, and attempt finding the best image according to the respective criterion. Due to the high complexity of the enhancement criterion proposed, we employ an evolutionary algorithm (EA) as a global search strategy for the best enhancement. We compared our method with other automatic enhancement techniques, like contrast stretching and histogram equalization. Results obtained, both in terms of subjective and objective evaluation, show the superiority of our method.