基于颜色编码和图像隐写术的可逆灰度方法

针对现有方法存在合成灰度图像视觉质量欠佳、重建彩色图像还原度不足的问题，提出一种基于颜色编码和图像隐写术的可逆灰度方法。其利用可逆神经网络构建更高效的颜色编解码器，并引入密集卷积块和通道注意力机制进一步提升网络模型的性能，综合减少编解码过程中的颜色信息丢失。之后，为使灰度图像负载编码信息以及减小嵌入过程导致的图像失真，设计了一种基于修改方向的图像隐写算法，通过自适应权值参数选择，以接近最优的方式满足不同的嵌入容量需求，减少对灰度图像的修改。在Kodak和McMaster数据集上的实验表明，与现有代表性可逆灰度方法相比较，该方法能够生成质量更高的可逆灰度图像以及重建更加还原的彩色图像，在图像可视化时具有更好的视觉效果，在标准参考图像的相似性评价指标方面也取得了更优的性能。

Invertible grayscale method based on color coding and image steganography

To address the problems of poor visual quality of synthesized grayscale and insufficient restoration of reconstructed color image in existing methods, this paper proposed an invertible grayscale method based on color coding and image steganography(IG-CCIS). The proposed method utilized an invertible neural network(INN) to construct an efficient color codec, and introduced dense convolutional blocks and channel attention mechanisms to further improve the performance of the network model, comprehensively reducing the loss of color information. In addition, in order to load encoded information into grayscale images and reduce image distortion caused by the embedding processed, it designed an image steganography algorithm based on exploiting modification direction(EMD). Through adaptive weight parameter selection, it could meet different embedding capacity requirements in a near-optimal manner and reduce the modification of grayscale images. Experimental tested on Kodak and McMaster datasets show that compared with existing representative reversible grayscale methods, the proposed method can generate better-quality reversible grayscale images and reconstruct more realistic color images, with better visual effects in image visualization. It also achieves better performance in terms of similarity evaluation metrics with standard reference images.