HSI色彩空间下的低照度遥感图像增强

为了提高低照度遥感图像的可视性,提出了利用改进的多尺度Retinex算法与局部对比度自适应调整相结合的方法来改善图像质量。首先,把原始图像变换到HSI色彩空间,有效分离H、S、I分量;然后,然后在保持色调分量H不变的前提下,对亮度分量I利用改进的多尺度Retinex算法进行处理,对整幅图像进行亮度和对比度的初步调整,通过使用Sigmoid函数替换多尺度Retinex算法中的对数函数来减少数据丢失;为了使局部细节信息得到更好的改善,在利用改进的多尺度Retinex算法处理后进行自适应局部对比度增强,提高图像局部对比度;对饱和度分量S采用分段线性增强的方法进行处理;最后,将处理后的图像变换回到RGB空间。实验结果表明:图像信息熵由5.79提高至6.65;图像感兴趣区域的局部对比度由0.695提高至0.701,图像质量以及利用价值得到了提升。

Low-illumination remote sensing image enhancement in HSI color space

In order to improve the visibility of low-illumination remote sensing images, an improved multiscale Retinex combined with a local contrast adaptive adjustment method was proposed. First, the original image was transformed into HSI color space and the hue component H, saturation component S, and brightness component I were effectively separated. The H component was unchanged, and an improved multiscale Retinex algorithm was applied to process the I component, to improve the overall brightness and contrast of the image. In this case, the Sigmoid function was used to replace the logarithm function in the multiscale Retinex algorithm to reduce the loss of image data. In order to improve the local detail information, local contrast adaptive enhancement was performed via image processing. Then the component S was processed by piecewise linear enhancement. Finally, the processed image was transformed to RGB color space. The experimental results indicate that the entropy of the image information is increased from 5.79 to 6.65, and the local contrast of the image interest area increased from 0.695 to 0.701. This indicates that the image quality and the applied value were effectively improved.