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Information 2017, 8(4), 135; doi:10.3390/info8040135

Enhancement of Low Contrast Images Based on Effective Space Combined with Pixel Learning

1
Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, #3888 Dongnanhu Road, Changchun 130033, China
2
University of Chinese Academy of Science, #19 Yuquan Road, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Received: 19 September 2017 / Revised: 27 October 2017 / Accepted: 27 October 2017 / Published: 1 November 2017
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Abstract

Images captured in bad conditions often suffer from low contrast. In this paper, we proposed a simple, but efficient linear restoration model to enhance the low contrast images. The model’s design is based on the effective space of the 3D surface graph of the image. Effective space is defined as the minimum space containing the 3D surface graph of the image, and the proportion of the pixel value in the effective space is considered to reflect the details of images. The bright channel prior and the dark channel prior are used to estimate the effective space, however, they may cause block artifacts. We designed the pixel learning to solve this problem. Pixel learning takes the input image as the training example and the low frequency component of input as the label to learn (pixel by pixel) based on the look-up table model. The proposed method is very fast and can restore a high-quality image with fine details. The experimental results on a variety of images captured in bad conditions, such as nonuniform light, night, hazy and underwater, demonstrate the effectiveness and efficiency of the proposed method. View Full-Text
Keywords: image enhancement; restoration model; nonilluminated; nighttime; dehaze; nighttime; underwater image enhancement; restoration model; nonilluminated; nighttime; dehaze; nighttime; underwater
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Li, G.; Li, G.; Han, G. Enhancement of Low Contrast Images Based on Effective Space Combined with Pixel Learning. Information 2017, 8, 135.

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