Preprint Article Version 1 This version is not peer-reviewed

A Cuckoo Based Optimization Approach for Image Enhancement

Version 1 : Received: 28 June 2018 / Approved: 9 July 2018 / Online: 9 July 2018 (05:07:27 CEST)

How to cite: Narang, D.; Kumar, L.; Kumar, P. A Cuckoo Based Optimization Approach for Image Enhancement. Preprints 2018, 2018070126 (doi: 10.20944/preprints201807.0126.v1). Narang, D.; Kumar, L.; Kumar, P. A Cuckoo Based Optimization Approach for Image Enhancement. Preprints 2018, 2018070126 (doi: 10.20944/preprints201807.0126.v1).

Abstract

The notion of enhancement of the image is to ameliorate the perceptibility of information contained in an image. In the present research, a novel technique for the enhancement of image quality is propounded using fuzzy logic technique with a cuckoo optimization algorithm. Generally, the image is transformed from RGB domain to HSV domain keeping the color information intact within the image. The image has been categorized into three regions: underexposed, overexposed and mixed region on the basis of two threshold values. For the fuzzification of under and overexposed area the degree of membership is defined by the Gaussian membership, while the mixed area is fuzzified by parametric sigmoid function. The key parameters like visual factors and fuzzy contrast provide the quantitative analysis of an image. An objective function is framed which involves entropy and visual factor has been optimized by a new evolutionary cuckoo optimization algorithm. The results procured after simulation by the cuckoo optimization algorithm are compared with Bacterial foraging algorithm and ant colony optimization based image enhancement and this approach is found to be improved.

Subject Areas

image enhancement; cuckoo optimization; entropy and visual factor

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