An Efficient Innovative Approach Towards Color Image Enhancement

An Efficient Innovative Approach Towards Color Image Enhancement

Dibya Jyoti Bora (Department of Computer Science and Applications, Barkatullah University, Bhopal, India)
Copyright: © 2018 |Pages: 18
DOI: 10.4018/IJIRR.2018010102

Abstract

Image Enhancement works as a first mandatory criteria for an efficient image analysis task. Removing noises and managing the contrast are the two major tasks that need to be accomplished in an image enhancement process. In this article, an innovative approach for color image enhancement is proposed. The proposed approach is a two-step technique. The first step is the noise removal step. Here, an improved median filter, Improved_Median(), is introduced to smooth the noises which exist in the original color image. Then, in the second step, local contrast enhancement is done. For that, an improved version of CLAHE, AA_CLAHE() is proposed for the local contrast management of the filtered image. The V-channel of HSV color space is used for the color computations involved in the local contrast management process. The overall enhancement done by the proposed approach is found to be satisfactory and outperforms the same produced by other state-of-the-art algorithms through experiments on several noisy and poor contrast color images obtained from different standards databases.
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Yadav et al. (2010) enhanced the visibility level of the foggy image with CLAHE. They enhanced the video quality in real time video system. The video frames are read one at a time. Intensity is adjusted for the foggy frame. First RGB image is converted to gray level and then CLAHE is applied. The resultant enhanced frame is a new structure. Finally, the enhanced De-foggy video is obtained after processing every frame with the above-mentioned step.

Zhao et al. (2010) proposed a novel method to achieve real-time subject-independent automatic facial feature enhancement and detection by combining CLAHE and multi-step integral projection. First, they used a sigma filter to remove noises. After that, they applied CLAHE for enhancing the facial features of the noise-free image obtained from the first step. They then did a multi-step integral projection to detect the useful facial feature regions automatically. Finally, Gabor transformation is used to extract the detected facial feature region and SVM classified the final facial expression recognition. They tested their proposed approach on JAFFE database and claimed a high recognition rate of 95.318% on trained data.

A new method called mixture Contrast Limited Adaptive Histogram Equalization (CLAHE) color models is proposed for underwater image enhancement (Hitam et al., 2013). The proposed method operates CLAHE on RGB and HSV and both the results are combined together using Euclidean norm. The experimental results prove that the proposed approach significantly improves the contrast of underwater images and also reduces noise and artifacts.

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