Fusion-Based Backlit Image Enhancement Using Multiple S-Type Transformations For Convex Combination Coefficients
Fusion-Based Backlit Image Enhancement Using Multiple S-Type Transformations For Convex Combination Coefficients
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DOI：
10.1109/icip46576.2022.9897370
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发表时间：
2022-10
期刊：
影响因子：
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通讯作者：
Yoshiaki Ueda;Takanori Koga;N. Suetake
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文献类型：
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作者：
Yoshiaki Ueda;Takanori Koga;N. Suetake
背光图像包含暗区和亮区，这些区域的可见性很低。提高这些图像的可见性对于物体识别是很重要的。一般来说，基于色调映射的全局对比度增强方法难以提高背光图像的局部对比度，因为亮度直方图失真严重。另一方面，局部对比度增强通过增强局部强度差来提高局部区域的可见性。然而，由于过度增强或亮度顺序混乱，图像可能会被转换成不自然的图像。本文提出了一种基于融合的背光图像增强方法。具体来说，我们使用不同的s型曲线对凸组合系数进行图像增强，以产生多种增强结果。通过对增强图像的组合，可以得到全局增强图像和局部增强图像。实验结果表明，该方法能够生成具有自然印象的全局增强图像和局部增强图像。
The backlit image contains both dark and bright regions, and the visibility of such regions is low. It is important to improve the visibility of such images for object recognition. In general, it is difficult to improve the local contrast of back-lit images by using a global contrast enhancement method based on tone mapping because the intensity histogram is severely distorted. On the other hand, local contrast enhancement improves the visibility in the local regions by enhancing local intensity differences. However, the image may be transformed into an unnatural image because of over-enhancement or disordered lightness order. In this paper, we propose a fusion-based backlit image enhancement method. Concretely, we perform image enhancement using various S-type curves for convex combination coefficients to produce multiple enhancement results. By combining enhanced images, it is possible to obtain global and local enhanced images. Through the experiments, we showed that the proposed method produces global and local enhanced images giving natural impression.