Zhao Yang, Wang Ronggang, Jia Wei, Zuo Wangmeng, Liu Xiaoping, Gao Wen
IEEE Trans Image Process. 2019 Jan 7. doi: 10.1109/TIP.2019.2891131.
Bit-depth expansion (BDE) is important for displaying a low bit-depth image in a high bit-depth monitor. Current BDE algorithms often utilize traditional methods to fill the missing least significant bits and suffer from multiple kinds of perceivable artifacts. In this paper, we present a deep residual network-based method for BDE. Based on the different properties of flat and non-flat areas, two channels are proposed to reconstruct these two kinds of areas, respectively. Moreover, a simple yet efficient local adaptive adjustment preprocessing is presented in the flat-area-channel. By combining the benefits of both the traditional debanding strategy and network-based reconstruction, the proposed method can further promote the subjective quality of the flat area. Experimental results on several image sets demonstrate that the proposed BDE network can obtain favorable visual quality as well as decent quantitative performance.
位深度扩展（BDE）对于在高位深度显示器上显示低位深度图像很重要。当前的BDE算法通常使用传统方法来填充缺失的最低有效位，并且会出现多种可感知的伪像。在本文中，我们提出了一种基于深度残差网络的BDE方法。基于平坦区域和非平坦区域的不同属性，提出了两个通道分别重建这两种区域。此外，在平坦区域通道中提出了一种简单而有效的局部自适应调整预处理。通过结合传统去带策略和基于网络的重建的优点，所提出的方法可以进一步提高平坦区域的主观质量。在几个图像集上的实验结果表明，所提出的BDE网络可以获得良好的视觉质量以及不错的定量性能。
