基于辅助变量增强的可逆彩色图像灰度化

Invertible Color Image Decolorization Based on Variable Augmented Network

彩色图像灰度化是一种被广泛应用于各个领域的图像压缩方式,但很少有研究关注彩色图像与灰度图像之间的相互转换技术。该文运用深度学习,创新性地提出了一种基于辅助变量增强的可逆彩色图像灰度化方法。该方法使用变量增强技术来保证输出与输入变量通道数相同以满足网络的可逆特性。具体来说,该方法通过可逆神经网络的正向过程实现彩色图像灰度化,逆向过程实现灰度图像的色彩复原。将所提方法在VOC2012,NCD和Wallpaper数据集上进行定性和定量比较。实验结果表明,所提方法在评价指标上均获得了更好的结果。无论是在全局还是局部,生成图像都可以最大程度地保留亮度、颜色对比度和结构相关性等特征。

Decolorization is an image compression method widely used in various fields,but few researches focus on the mutual conversion technology of color image and grayscale image.In this paper,a deep learning method is used to propose innovatively an invertible decolorization method based on variable augmentation.This method uses variable augmentation technology to ensure that the output has the same number of channels as the input variable,which satisfies the reversible characteristics of the network.Specifically,the proposed method realizes the decolorization through the forward process of the invertible neural network,and realizes the color restoration of grayscale images through the reverse process.The proposed method performs qualitative and quantitative comparisons on VOC2012,NCD,Wallpaper datasets.The experimental results show that the proposed method achieves better results in the evaluation indicators.The quality of the generated images can preserve the characteristics of brightness,color contrast and structural correlation to the greatest extent,both globally and locally.