基于语义分割的红外图像增强方法

针对对比度受限的自适应直方图均衡化（contrast limited adaptive histogram equalization, CLAHE）强行分块造成的视觉不自然现象，本文提出了一种基于语义分割的红外图像增强方法。语义分割网络将整个红外图像分割成种类块而不是传统的矩形图像块。然后，每个种类块各自进行对比度受限的直方图均衡化，以减少过度增强。最后，采用了一种新的边缘过渡方法来避免种类块之间的突变。实验结果表明，本文所提出的红外图像增强方法在对比度和熵上优于其他对比算法，而且避免了传统CLAHE的视觉不自然现象，具有更好的视觉效果。

An Infrared Image Enhancement Method Based on Semantic Segmentation

To solve the problem of visual unnaturalness caused by contrast-limited adaptive histogram equalization (CLAHE) forced blocking, this study proposes an infrared image enhancement method based on semantic segmentation. The semantic segmentation network segments the entire infrared image into category blocks instead of traditional rectangular image blocks. Each category block is individually subjected to contrast-limited histogram equalization to reduce over-enhancement. Finally, a new edge transition method is introduced to avoid abruptness between category blocks. The experimental results show that the proposed image enhancement method outperforms other contrast algorithms in terms of contrast and entropy and avoids the visual unnaturalness of traditional CLAHE with better visual effects.