Low-Artifact and Fast Backlit Image Enhancement Method Based on Suppression of Lightness Order Error
Many image enhancement methods have been proposed to improve the visibility of backlit images. Although these methods can effectively improve the visibility of the subject and background compared to standard image enhancement methods, they may result in image quality degradation owing to non-negligi...
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Published in | IEEE Access Vol. 11; pp. 121231 - 121245 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Piscataway
IEEE
2023
Institute of Electrical and Electronics Engineers (IEEE) The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subjects | |
Online Access | Get full text |
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Summary: | Many image enhancement methods have been proposed to improve the visibility of backlit images. Although these methods can effectively improve the visibility of the subject and background compared to standard image enhancement methods, they may result in image quality degradation owing to non-negligible artifacts. In many cases, such artifacts are caused by a significant change in the Lightness Order Error (LOE) between the original and processed images. To address this problem, this paper proposes a low-artifact and fast backlit image enhancement method to effectively improve the visibility of images by suppressing the LOE. The proposed method uses adaptive luminance correction to generate lightness-enhanced images of the dark and bright areas of the backlit image. These images are then fused based on a weight map to calculate the lightness of the output image with a lower LOE. The final output, i.e., the enhanced color image, is obtained by multiplying the input color image by the ratio of the lightness component of the input image to the enhanced lightness component. The experimental results demonstrate the superiority of the proposed method in terms of low artifacts, natural enhancement, and high processing speed based on straightforward processing. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
ISSN: | 2169-3536 2169-3536 |
DOI: | 10.1109/ACCESS.2023.3328534 |