Preprint Article Version 1 This version is not peer-reviewed

Contrast Limited Adaptive Histogram Equalization Based Fusion for Underwater Image Enhancement

Version 1 : Received: 13 March 2017 / Approved: 14 March 2017 / Online: 14 March 2017 (17:52:48 CET)

How to cite: MA, J.; Fan, X.; Yang, S.X.; Zhang, X.; Zhu, X. Contrast Limited Adaptive Histogram Equalization Based Fusion for Underwater Image Enhancement. Preprints 2017, 2017030086 (doi: 10.20944/preprints201703.0086.v1). MA, J.; Fan, X.; Yang, S.X.; Zhang, X.; Zhu, X. Contrast Limited Adaptive Histogram Equalization Based Fusion for Underwater Image Enhancement. Preprints 2017, 2017030086 (doi: 10.20944/preprints201703.0086.v1).

Abstract

In order to improve contrast and restore color for underwater image captured by camera sensors without suffering from insufficient details and color cast, a fusion algorithm for image enhancement in different color spaces based on contrast limited adaptive histogram equalization (CLAHE) is proposed in this article. The original color image is first converted from RGB color space to two different special color spaces: YIQ and HSI. The color space conversion from RGB to YIQ is a linear transformation, while the RGB to HSI conversion is nonlinear. Then, the algorithm separately operates CLAHE in YIQ and HSI color spaces to obtain two different enhancement images. The luminance component (Y) in the YIQ color space and the intensity component (I) in the HSI color space are enhanced with CLAHE algorithm. The CLAHE has two key parameters: Block Size and Clip Limit, which mainly control the quality of CLAHE enhancement image. After that, the YIQ and HSI enhancement images are respectively converted backward to RGB color. When the three components of red, green, and blue are not coherent in the YIQ-RGB or HSI-RGB images, the three components will have to be harmonized with the CLAHE algorithm in RGB space. Finally, with 4 direction Sobel edge detector in the bounded general logarithm ratio operation, a self-adaptive weight selection nonlinear image enhancement is carried out to fuse YIQ-RGB and HSI-RGB images together to achieve the final fused image. The enhancement fusion algorithm has two key factors: average of Sobel edge detector and fusion coefficient, and these two factors determine the effects of enhancement fusion algorithm. A series of evaluate metrics such as mean, contrast, entropy, colorfulness metric (CM), mean square error (MSE) and peak signal to noise ratio (PSNR) are used to assess the proposed enhancement algorithm. The experiments results showed that the proposed algorithm provides more detail enhancement and higher values of colorfulness restoration as compared to other existing image enhancement algorithms. The proposed algorithm can suppress effectively noise interference, improve the image quality for underwater image availably.

Subject Areas

image enhancement; image fusion; color space; edge detector; underwater image

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